fix(expand-task): Ensure advanced parsing logic works and trimmed AI response properly if any jsonToParse modifications need to be made on initial parse of response.

* feat: added complexity score handling to list tasks

* feat: added handling for complexity score in find task by id

* test: remove console dir

* chore: add changeset

* format: fixed formatting issues

* ref: reorder imports

* feat: updated handling for findTaskById to take complexityReport as input

* test: fix findTaskById complexity report testcases

* fix: added handling for complexity report path

* chore: add changeset

* fix: moved complexity report handling to list tasks rather than list tasks direct

* fix: add complexity handling to next task in list command

* fix: added handling for show cli

* fix: fixed next cli command handling

* fix: fixed handling for complexity report path in mcp

* feat: added handling to get-task

* feat: added handling for next-task in mcp

* feat: add handling for report path override

* chore: remove unecessary changeset

* ref: remove unecessary comments

* feat: update list and find next task

* fix: fixed running tests

* fix: fixed findTaskById

* fix: fixed findTaskById and tests

* fix: fixed addComplexityToTask util

* fix: fixed mcp server project root input

* chore: cleanup

---------

Co-authored-by: Shrey Paharia <shreypaharia@gmail.com>

.changeset/forty-plums-stay.md

@@ -0,0 +1,5 @@
+---
+'task-master-ai': minor
+---
+
+.taskmasterconfig now supports a baseUrl field per model role (main, research, fallback), allowing endpoint overrides for any provider.

.changeset/many-wasps-sell.md

@@ -0,0 +1,5 @@
+---
+'task-master-ai': patch
+---
+
+Task Master no longer tells you to update when you're already up to date

.changeset/nice-lies-cover.md

@@ -0,0 +1,5 @@
+---
+'task-master-ai': patch
+---
+
+Adds costs information to AI commands using input/output tokens and model costs.

.changeset/pre.json

@@ -1,30 +1,23 @@
 {
-  "mode": "pre",
+  "mode": "exit",
   "tag": "rc",
   "initialVersions": {
-    "task-master-ai": "0.13.1"
+    "task-master-ai": "0.13.2"
   },
   "changesets": [
     "beige-doodles-type",
-    "beige-rats-accept",
+    "floppy-plants-marry",
-    "blue-spies-kick",
+    "forty-plums-stay",
-    "cuddly-zebras-matter",
+    "many-wasps-sell",
-    "curvy-candies-eat",
-    "easy-toys-wash",
-    "every-stars-sell",
-    "fine-monkeys-eat",
-    "fine-signs-add",
-    "gentle-views-jump",
-    "mighty-mirrors-watch",
-    "neat-donkeys-shave",
-    "nine-rocks-sink",
-    "ninety-ghosts-relax",
-    "ninety-wombats-pull",
-    "public-cooks-fetch",
     "red-oranges-attend",
     "red-suns-wash",
-    "tricky-papayas-hang",
+    "sharp-dingos-melt",
-    "violet-papayas-see",
+    "six-cloths-happen",
-    "violet-parrots-march"
+    "slow-singers-swim",
+    "social-masks-fold",
+    "soft-zoos-flow",
+    "ten-ways-mate",
+    "tricky-wombats-spend",
+    "wide-eyes-relax"
   ]
 }

.changeset/sharp-dingos-melt.md

@@ -0,0 +1,5 @@
+---
+'task-master-ai': patch
+---
+
+Fix the error handling of task status settings

.changeset/six-cloths-happen.md

@@ -0,0 +1,7 @@
+---
+'task-master-ai': patch
+---
+
+Remove caching layer from MCP direct functions for task listing, next task, and complexity report
+
+- Fixes issues users where having where they were getting stale data

.changeset/slow-singers-swim.md

@@ -0,0 +1,5 @@
+---
+'task-master-ai': patch
+---
+
+Fix for issue #409 LOG_LEVEL Pydantic validation error

.changeset/small-toys-fly.md

@@ -0,0 +1,7 @@
+---
+'task-master-ai': patch
+---
+
+Small fixes
+    - `next` command no longer incorrectly suggests that subtasks be broken down into subtasks in the CLI
+    - fixes the `append` flag so it properly works in the CLI

.changeset/social-masks-fold.md

@@ -0,0 +1,5 @@
+---
+'task-master-ai': minor
+---
+
+Display task complexity scores in task lists, next task, and task details views.

.changeset/ten-ways-mate.md

@@ -0,0 +1,5 @@
+---
+'task-master-ai': patch
+---
+
+Fix default fallback model and maxTokens in Taskmaster initialization

.changeset/tricky-wombats-spend.md

@@ -0,0 +1,5 @@
+---
+'task-master-ai': patch
+---
+
+Fix bug when updating tasks on the MCP server (#412)

.cursor/rules/ai_services.mdc

@@ -25,6 +25,7 @@ This document outlines the architecture and usage patterns for interacting with
     *   Implements **retry logic** for specific API errors (`_attemptProviderCallWithRetries`).
     *   Resolves API keys automatically via `_resolveApiKey` (using `resolveEnvVariable`).
     *   Maps requests to the correct provider implementation (in `src/ai-providers/`) via `PROVIDER_FUNCTIONS`.
+    *   Returns a structured object containing the primary AI result (`mainResult`) and telemetry data (`telemetryData`). See [`telemetry.mdc`](mdc:.cursor/rules/telemetry.mdc) for details on how this telemetry data is propagated and handled.
 
 *   **Provider Implementations (`src/ai-providers/*.js`):**
     *   Contain provider-specific wrappers around Vercel AI SDK functions (`generateText`, `generateObject`).

.cursor/rules/architecture.mdc

@@ -42,6 +42,7 @@ alwaysApply: false
       - Resolves API keys (from `.env` or `session.env`).
       - Implements fallback and retry logic.
       - Orchestrates calls to provider-specific implementations (`src/ai-providers/`).
+    - Telemetry data generated by the AI service layer is propagated upwards through core logic, direct functions, and MCP tools. See [`telemetry.mdc`](mdc:.cursor/rules/telemetry.mdc) for the detailed integration pattern.
 
   - **[`src/ai-providers/*.js`](mdc:src/ai-providers/): Provider-Specific Implementations**
     - **Purpose**: Provider-specific wrappers for Vercel AI SDK functions.

.cursor/rules/dev_workflow.mdc

@@ -116,7 +116,7 @@ Taskmaster configuration is managed through two main mechanisms:
     *   For MCP/Cursor integration, configure these keys in the `env` section of `.cursor/mcp.json`.
     *   Available keys/variables: See `assets/env.example` or the Configuration section in the command reference (previously linked to `taskmaster.mdc`).
 
-**Important:** Non-API key settings (like model selections, `MAX_TOKENS`, `LOG_LEVEL`) are **no longer configured via environment variables**. Use the `task-master models` command (or `--setup` for interactive configuration) or the `models` MCP tool.
+**Important:** Non-API key settings (like model selections, `MAX_TOKENS`, `TASKMASTER_LOG_LEVEL`) are **no longer configured via environment variables**. Use the `task-master models` command (or `--setup` for interactive configuration) or the `models` MCP tool.
 **If AI commands FAIL in MCP** verify that the API key for the selected provider is present in the `env` section of `.cursor/mcp.json`.
 **If AI commands FAIL in CLI** verify that the API key for the selected provider is present in the `.env` file in the root of the project.
 

.cursor/rules/glossary.mdc

@@ -3,7 +3,6 @@ description: Glossary of other Cursor rules
 globs: **/*
 alwaysApply: true
 ---
-
 # Glossary of Task Master Cursor Rules
 
 This file provides a quick reference to the purpose of each rule file located in the `.cursor/rules` directory.
@@ -23,4 +22,5 @@ This file provides a quick reference to the purpose of each rule file located in
 - **[`tests.mdc`](mdc:.cursor/rules/tests.mdc)**: Guidelines for implementing and maintaining tests for Task Master CLI.
 - **[`ui.mdc`](mdc:.cursor/rules/ui.mdc)**: Guidelines for implementing and maintaining user interface components.
 - **[`utilities.mdc`](mdc:.cursor/rules/utilities.mdc)**: Guidelines for implementing utility functions.
+- **[`telemetry.mdc`](mdc:.cursor/rules/telemetry.mdc)**: Guidelines for integrating AI usage telemetry across Task Master.
 

.cursor/rules/mcp.mdc

@@ -522,3 +522,8 @@ Follow these steps to add MCP support for an existing Task Master command (see [
       // Add more functions as implemented
     };
     ```
+
+## Telemetry Integration
+
+- Direct functions calling core logic that involves AI should receive and pass through `telemetryData` within their successful `data` payload. See [`telemetry.mdc`](mdc:.cursor/rules/telemetry.mdc) for the standard pattern.
+- MCP tools use `handleApiResult`, which ensures the `data` object (potentially including `telemetryData`) from the direct function is correctly included in the final response.

.cursor/rules/new_features.mdc

@@ -3,7 +3,6 @@ description: Guidelines for integrating new features into the Task Master CLI
 globs: scripts/modules/*.js
 alwaysApply: false
 ---
-
 # Task Master Feature Integration Guidelines
 
 ## Feature Placement Decision Process
@@ -196,6 +195,8 @@ The standard pattern for adding a feature follows this workflow:
   - ✅ **DO**: If an MCP tool fails with vague errors (e.g., JSON parsing issues like `Unexpected token ... is not valid JSON`), **try running the equivalent CLI command directly in the terminal** (e.g., `task-master expand --all`). CLI output often provides much more specific error messages (like missing function definitions or stack traces from the core logic) that pinpoint the root cause.
   - ❌ **DON'T**: Rely solely on MCP logs if the error is unclear; use the CLI as a complementary debugging tool for core logic issues.
 
+- **Telemetry Integration**: Ensure AI calls correctly handle and propagate `telemetryData` as described in [`telemetry.mdc`](mdc:.cursor/rules/telemetry.mdc).
+
 ```javascript
 // 1. CORE LOGIC: Add function to appropriate module (example in task-manager.js)
 /**

.cursor/rules/telemetry.mdc

@@ -0,0 +1,228 @@
+---
+description: Guidelines for integrating AI usage telemetry across Task Master.
+globs: scripts/modules/**/*.js,mcp-server/src/**/*.js
+alwaysApply: true
+---
+
+# AI Usage Telemetry Integration
+
+This document outlines the standard pattern for capturing, propagating, and handling AI usage telemetry data (cost, tokens, model, etc.) across the Task Master stack. This ensures consistent telemetry for both CLI and MCP interactions.
+
+## Overview
+
+Telemetry data is generated within the unified AI service layer ([`ai-services-unified.js`](mdc:scripts/modules/ai-services-unified.js)) and then passed upwards through the calling functions.
+
+- **Data Source**: [`ai-services-unified.js`](mdc:scripts/modules/ai-services-unified.js) (specifically its `generateTextService`, `generateObjectService`, etc.) returns an object like `{ mainResult: AI_CALL_OUTPUT, telemetryData: TELEMETRY_OBJECT }`.
+- **`telemetryData` Object Structure**:
+  ```json
+  {
+    "timestamp": "ISO_STRING_DATE",
+    "userId": "USER_ID_FROM_CONFIG",
+    "commandName": "invoking_command_or_tool_name",
+    "modelUsed": "ai_model_id",
+    "providerName": "ai_provider_name",
+    "inputTokens": NUMBER,
+    "outputTokens": NUMBER,
+    "totalTokens": NUMBER,
+    "totalCost": NUMBER, // e.g., 0.012414
+    "currency": "USD" // e.g., "USD"
+  }
+  ```
+
+## Integration Pattern by Layer
+
+The key principle is that each layer receives telemetry data from the layer below it (if applicable) and passes it to the layer above it, or handles it for display in the case of the CLI.
+
+### 1. Core Logic Functions (e.g., in `scripts/modules/task-manager/`)
+
+Functions in this layer that invoke AI services are responsible for handling the `telemetryData` they receive from [`ai-services-unified.js`](mdc:scripts/modules/ai-services-unified.js).
+
+- **Actions**:
+    1.  Call the appropriate AI service function (e.g., `generateObjectService`).
+        -   Pass `commandName` (e.g., `add-task`, `expand-task`) and `outputType` (e.g., `cli` or `mcp`) in the `params` object to the AI service. The `outputType` can be derived from context (e.g., presence of `mcpLog`).
+    2.  The AI service returns an object, e.g., `aiServiceResponse = { mainResult: {/*AI output*/}, telemetryData: {/*telemetry data*/} }`.
+    3.  Extract `aiServiceResponse.mainResult` for the core processing.
+    4.  **Must return an object that includes `aiServiceResponse.telemetryData`**.
+        Example: `return { operationSpecificData: /*...*/, telemetryData: aiServiceResponse.telemetryData };`
+
+- **CLI Output Handling (If Applicable)**:
+    -   If the core function also handles CLI output (e.g., it has an `outputFormat` parameter that can be `'text'` or `'cli'`):
+        1.  Check if `outputFormat === 'text'` (or `'cli'`).
+        2.  If so, and if `aiServiceResponse.telemetryData` is available, call `displayAiUsageSummary(aiServiceResponse.telemetryData, 'cli')` from [`scripts/modules/ui.js`](mdc:scripts/modules/ui.js).
+        - This ensures telemetry is displayed directly to CLI users after the main command output.
+
+- **Example Snippet (Core Logic in `scripts/modules/task-manager/someAiAction.js`)**:
+  ```javascript
+  import { generateObjectService } from '../ai-services-unified.js';
+  import { displayAiUsageSummary } from '../ui.js';
+
+  async function performAiRelatedAction(params, context, outputFormat = 'text') {
+    const { commandNameFromContext, /* other context vars */ } = context;
+    let aiServiceResponse = null;
+
+    try {
+      aiServiceResponse = await generateObjectService({
+        // ... other parameters for AI service ...
+        commandName: commandNameFromContext || 'default-action-name',
+        outputType: context.mcpLog ? 'mcp' : 'cli' // Derive outputType
+      });
+
+      const usefulAiOutput = aiServiceResponse.mainResult.object;
+      // ... do work with usefulAiOutput ...
+
+      if (outputFormat === 'text' && aiServiceResponse.telemetryData) {
+        displayAiUsageSummary(aiServiceResponse.telemetryData, 'cli');
+      }
+
+      return {
+        actionData: /* results of processing */,
+        telemetryData: aiServiceResponse.telemetryData
+      };
+    } catch (error) {
+      // ... handle error ...
+      throw error;
+    }
+  }
+  ```
+
+### 2. Direct Function Wrappers (in `mcp-server/src/core/direct-functions/`)
+
+These functions adapt core logic for the MCP server, ensuring structured responses.
+
+- **Actions**:
+    1.  Call the corresponding core logic function.
+        -   Pass necessary context (e.g., `session`, `mcpLog`, `projectRoot`).
+        -   Provide the `commandName` (typically derived from the MCP tool name) and `outputType: 'mcp'` in the context object passed to the core function.
+        -   If the core function supports an `outputFormat` parameter, pass `'json'` to suppress CLI-specific UI.
+    2.  The core logic function returns an object (e.g., `coreResult = { actionData: ..., telemetryData: ... }`).
+    3.  Include `coreResult.telemetryData` as a field within the `data` object of the successful response returned by the direct function.
+
+- **Example Snippet (Direct Function `someAiActionDirect.js`)**:
+  ```javascript
+  import { performAiRelatedAction } from '../../../../scripts/modules/task-manager/someAiAction.js'; // Core function
+  import { createLogWrapper } from '../../tools/utils.js'; // MCP Log wrapper
+
+  export async function someAiActionDirect(args, log, context = {}) {
+    const { session } = context;
+    // ... prepare arguments for core function from args, including args.projectRoot ...
+
+    try {
+      const coreResult = await performAiRelatedAction(
+        { /* parameters for core function */ },
+        { // Context for core function
+          session,
+          mcpLog: createLogWrapper(log),
+          projectRoot: args.projectRoot,
+          commandNameFromContext: 'mcp_tool_some_ai_action', // Example command name
+          outputType: 'mcp'
+        },
+        'json' // Request 'json' output format from core function
+      );
+
+      return {
+        success: true,
+        data: {
+          operationSpecificData: coreResult.actionData,
+          telemetryData: coreResult.telemetryData // Pass telemetry through
+        }
+      };
+    } catch (error) {
+      // ... error handling, return { success: false, error: ... } ...
+    }
+  }
+  ```
+
+### 3. MCP Tools (in `mcp-server/src/tools/`)
+
+These are the exposed endpoints for MCP clients.
+
+- **Actions**:
+    1.  Call the corresponding direct function wrapper.
+    2.  The direct function returns an object structured like `{ success: true, data: { operationSpecificData: ..., telemetryData: ... } }` (or an error object).
+    3.  Pass this entire result object to `handleApiResult(result, log)` from [`mcp-server/src/tools/utils.js`](mdc:mcp-server/src/tools/utils.js).
+    4.  `handleApiResult` ensures that the `data` field from the direct function's response (which correctly includes `telemetryData`) is part of the final MCP response.
+
+- **Example Snippet (MCP Tool `some_ai_action.js`)**:
+  ```javascript
+  import { someAiActionDirect } from '../core/task-master-core.js';
+  import { handleApiResult, withNormalizedProjectRoot } from './utils.js';
+  // ... zod for parameters ...
+
+  export function registerSomeAiActionTool(server) {
+    server.addTool({
+      name: "some_ai_action",
+      // ... description, parameters ...
+      execute: withNormalizedProjectRoot(async (args, { log, session }) => {
+        try {
+          const resultFromDirectFunction = await someAiActionDirect(
+            { /* args including projectRoot */ },
+            log,
+            { session }
+          );
+          return handleApiResult(resultFromDirectFunction, log); // This passes the nested telemetryData through
+        } catch (error) {
+          // ... error handling ...
+        }
+      })
+    });
+  }
+  ```
+
+### 4. CLI Commands (`scripts/modules/commands.js`)
+
+These define the command-line interface.
+
+- **Actions**:
+    1.  Call the appropriate core logic function.
+    2.  Pass `outputFormat: 'text'` (or ensure the core function defaults to text-based output for CLI).
+    3.  The core logic function (as per Section 1) is responsible for calling `displayAiUsageSummary` if telemetry data is available and it's in CLI mode.
+    4.  The command action itself **should not** call `displayAiUsageSummary` if the core logic function already handles this. This avoids duplicate display.
+
+- **Example Snippet (CLI Command in `commands.js`)**:
+  ```javascript
+  // In scripts/modules/commands.js
+  import { performAiRelatedAction } from './task-manager/someAiAction.js'; // Core function
+
+  programInstance
+    .command('some-cli-ai-action')
+    // ... .option() ...
+    .action(async (options) => {
+      try {
+        const projectRoot = findProjectRoot() || '.'; // Example root finding
+        // ... prepare parameters for core function from command options ...
+        await performAiRelatedAction(
+          { /* parameters for core function */ },
+          { // Context for core function
+            projectRoot,
+            commandNameFromContext: 'some-cli-ai-action',
+            outputType: 'cli'
+          },
+          'text' // Explicitly request text output format for CLI
+        );
+        // Core function handles displayAiUsageSummary internally for 'text' outputFormat
+      } catch (error) {
+        // ... error handling ...
+      }
+    });
+  ```
+
+## Summary Flow
+
+The telemetry data flows as follows:
+
+1.  **[`ai-services-unified.js`](mdc:scripts/modules/ai-services-unified.js)**: Generates `telemetryData` and returns `{ mainResult, telemetryData }`.
+2.  **Core Logic Function**:
+    *   Receives `{ mainResult, telemetryData }`.
+    *   Uses `mainResult`.
+    *   If CLI (`outputFormat: 'text'`), calls `displayAiUsageSummary(telemetryData)`.
+    *   Returns `{ operationSpecificData, telemetryData }`.
+3.  **Direct Function Wrapper**:
+    *   Receives `{ operationSpecificData, telemetryData }` from core logic.
+    *   Returns `{ success: true, data: { operationSpecificData, telemetryData } }`.
+4.  **MCP Tool**:
+    *   Receives direct function response.
+    *   `handleApiResult` ensures the final MCP response to the client is `{ success: true, data: { operationSpecificData, telemetryData } }`.
+5.  **CLI Command**:
+    *   Calls core logic with `outputFormat: 'text'`. Display is handled by core logic.
+
+This pattern ensures telemetry is captured and appropriately handled/exposed across all interaction modes.

.github/workflows/pre-release.yml

@@ -2,9 +2,6 @@ name: Pre-Release (RC)
 
 on:
   workflow_dispatch: # Allows manual triggering from GitHub UI/API
-  push:
-    branches:
-      - 'next'
 
 concurrency: pre-release-${{ github.ref }}
 
@@ -41,12 +38,10 @@ jobs:
           npx changeset pre enter rc
 
       - name: Version RC packages
-        run: |
+        run: npx changeset version
-          git config user.name "GitHub Actions"
+        env:
-          git config user.email "github-actions@example.com"
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          npx changeset version
+          NPM_TOKEN: ${{ secrets.NPM_TOKEN }}
-          git add .
-          git commit -m "chore: rc version bump" || echo "No changes to commit"
 
       - name: Create Release Candidate Pull Request or Publish Release Candidate to npm
         uses: changesets/action@v1
@@ -55,3 +50,13 @@ jobs:
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
           NPM_TOKEN: ${{ secrets.NPM_TOKEN }}
+
+      - name: Exit RC mode
+        run: npx changeset pre exit
+
+      - name: Commit & Push changes
+        uses: actions-js/push@master
+        with:
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+          branch: ${{ github.ref }}
+          message: 'chore: rc version bump'

.taskmasterconfig

@@ -1,31 +1,32 @@
 {
-  "models": {
+	"models": {
-    "main": {
+		"main": {
-      "provider": "anthropic",
+			"provider": "anthropic",
-      "modelId": "claude-3-7-sonnet-20250219",
+			"modelId": "claude-3-7-sonnet-20250219",
-      "maxTokens": 100000,
+			"maxTokens": 100000,
-      "temperature": 0.2
+			"temperature": 0.2
-    },
+		},
-    "research": {
+		"research": {
-      "provider": "perplexity",
+			"provider": "perplexity",
-      "modelId": "sonar-pro",
+			"modelId": "sonar-pro",
-      "maxTokens": 8700,
+			"maxTokens": 8700,
-      "temperature": 0.1
+			"temperature": 0.1
-    },
+		},
-    "fallback": {
+		"fallback": {
-      "provider": "anthropic",
+			"provider": "anthropic",
-      "modelId": "claude-3-7-sonnet-20250219",
+			"modelId": "claude-3-7-sonnet-20250219",
-      "maxTokens": 120000,
+			"maxTokens": 8192,
-      "temperature": 0.2
+			"temperature": 0.2
-    }
+		}
-  },
+	},
-  "global": {
+	"global": {
-    "logLevel": "info",
+		"logLevel": "info",
-    "debug": false,
+		"debug": false,
-    "defaultSubtasks": 5,
+		"defaultSubtasks": 5,
-    "defaultPriority": "medium",
+		"defaultPriority": "medium",
-    "projectName": "Taskmaster",
+		"projectName": "Taskmaster",
-    "ollamaBaseUrl": "http://localhost:11434/api",
+		"ollamaBaseUrl": "http://localhost:11434/api",
-    "azureOpenaiBaseUrl": "https://your-endpoint.openai.azure.com/"
+		"userId": "1234567890",
-  }
+		"azureOpenaiBaseUrl": "https://your-endpoint.openai.azure.com/"
-}
+	}
+}

CHANGELOG.md

@@ -1,5 +1,47 @@
 # task-master-ai
 
+## 0.14.0-rc.0
+
+### Minor Changes
+
+- [#521](https://github.com/eyaltoledano/claude-task-master/pull/521) [`ed17cb0`](https://github.com/eyaltoledano/claude-task-master/commit/ed17cb0e0a04dedde6c616f68f24f3660f68dd04) Thanks [@Crunchyman-ralph](https://github.com/Crunchyman-ralph)! - .taskmasterconfig now supports a baseUrl field per model role (main, research, fallback), allowing endpoint overrides for any provider.
+
+- [#528](https://github.com/eyaltoledano/claude-task-master/pull/528) [`58b417a`](https://github.com/eyaltoledano/claude-task-master/commit/58b417a8ce697e655f749ca4d759b1c20014c523) Thanks [@Crunchyman-ralph](https://github.com/Crunchyman-ralph)! - Display task complexity scores in task lists, next task, and task details views.
+
+### Patch Changes
+
+- [#478](https://github.com/eyaltoledano/claude-task-master/pull/478) [`4117f71`](https://github.com/eyaltoledano/claude-task-master/commit/4117f71c18ee4d321a9c91308d00d5d69bfac61e) Thanks [@joedanz](https://github.com/joedanz)! - Fix CLI --force flag for parse-prd command
+
+  Previously, the --force flag was not respected when running `parse-prd`, causing the command to prompt for confirmation or fail even when --force was provided. This patch ensures that the flag is correctly passed and handled, allowing users to overwrite existing tasks.json files as intended.
+
+  - Fixes #477
+
+- [#511](https://github.com/eyaltoledano/claude-task-master/pull/511) [`17294ff`](https://github.com/eyaltoledano/claude-task-master/commit/17294ff25918d64278674e558698a1a9ad785098) Thanks [@Crunchyman-ralph](https://github.com/Crunchyman-ralph)! - Task Master no longer tells you to update when you're already up to date
+
+- [#523](https://github.com/eyaltoledano/claude-task-master/pull/523) [`da317f2`](https://github.com/eyaltoledano/claude-task-master/commit/da317f2607ca34db1be78c19954996f634c40923) Thanks [@Crunchyman-ralph](https://github.com/Crunchyman-ralph)! - Fix the error handling of task status settings
+
+- [#527](https://github.com/eyaltoledano/claude-task-master/pull/527) [`a8dabf4`](https://github.com/eyaltoledano/claude-task-master/commit/a8dabf44856713f488960224ee838761716bba26) Thanks [@Crunchyman-ralph](https://github.com/Crunchyman-ralph)! - Remove caching layer from MCP direct functions for task listing, next task, and complexity report
+
+  - Fixes issues users where having where they were getting stale data
+
+- [#417](https://github.com/eyaltoledano/claude-task-master/pull/417) [`a1f8d52`](https://github.com/eyaltoledano/claude-task-master/commit/a1f8d52474fdbdf48e17a63e3f567a6d63010d9f) Thanks [@ksylvan](https://github.com/ksylvan)! - Fix for issue #409 LOG_LEVEL Pydantic validation error
+
+- [#501](https://github.com/eyaltoledano/claude-task-master/pull/501) [`0a61184`](https://github.com/eyaltoledano/claude-task-master/commit/0a611843b56a856ef0a479dc34078326e05ac3a8) Thanks [@Crunchyman-ralph](https://github.com/Crunchyman-ralph)! - Fix initial .env.example to work out of the box
+
+  - Closes #419
+
+- [#435](https://github.com/eyaltoledano/claude-task-master/pull/435) [`a96215a`](https://github.com/eyaltoledano/claude-task-master/commit/a96215a359b25061fd3b3f3c7b10e8ac0390c062) Thanks [@lebsral](https://github.com/lebsral)! - Fix default fallback model and maxTokens in Taskmaster initialization
+
+- [#517](https://github.com/eyaltoledano/claude-task-master/pull/517) [`e96734a`](https://github.com/eyaltoledano/claude-task-master/commit/e96734a6cc6fec7731de72eb46b182a6e3743d02) Thanks [@Crunchyman-ralph](https://github.com/Crunchyman-ralph)! - Fix bug when updating tasks on the MCP server (#412)
+
+- [#496](https://github.com/eyaltoledano/claude-task-master/pull/496) [`efce374`](https://github.com/eyaltoledano/claude-task-master/commit/efce37469bc58eceef46763ba32df1ed45242211) Thanks [@joedanz](https://github.com/joedanz)! - Fix duplicate output on CLI help screen
+
+  - Prevent the Task Master CLI from printing the help screen more than once when using `-h` or `--help`.
+  - Removed redundant manual event handlers and guards for help output; now only the Commander `.helpInformation` override is used for custom help.
+  - Simplified logic so that help is only shown once for both "no arguments" and help flag flows.
+  - Ensures a clean, branded help experience with no repeated content.
+  - Fixes #339
+
 ## 0.13.1
 
 ### Patch Changes

README.md

@@ -11,8 +11,20 @@ A task management system for AI-driven development with Claude, designed to work
 
 ## Requirements
 
+Taskmaster utilizes AI across several commands, and those require a separate API key. You can use a variety of models from different AI providers provided you add your API keys. For example, if you want to use Claude 3.7, you'll need an Anthropic API key.
+
+You can define 3 types of models to be used: the main model, the research model, and the fallback model (in case either the main or research fail). Whatever model you use, its provider API key must be present in either mcp.json or .env.
+
+At least one (1) of the following is required:
+
 - Anthropic API key (Claude API)
-- OpenAI SDK (for Perplexity API integration, optional)
+- OpenAI API key
+- Google Gemini API key
+- Perplexity API key (for research model)
+- xAI API Key (for research or main model)
+- OpenRouter API Key (for research or main model)
+
+Using the research model is optional but highly recommended. You will need at least ONE API key. Adding all API keys enables you to seamlessly switch between model providers at will.
 
 ## Quick Start
 

assets/.taskmasterconfig

@@ -14,8 +14,8 @@
 		},
 		"fallback": {
 			"provider": "anthropic",
-			"modelId": "claude-3.5-sonnet-20240620",
+			"modelId": "claude-3-5-sonnet-20240620",
-			"maxTokens": 120000,
+			"maxTokens": 8192,
 			"temperature": 0.1
 		}
 	},

assets/.windsurfrules

@@ -198,7 +198,7 @@ alwaysApply: true
   - **MAX_TOKENS** (Default: `"4000"`): Maximum tokens for responses (Example: `MAX_TOKENS=8000`)
   - **TEMPERATURE** (Default: `"0.7"`): Temperature for model responses (Example: `TEMPERATURE=0.5`)
   - **DEBUG** (Default: `"false"`): Enable debug logging (Example: `DEBUG=true`)
-  - **LOG_LEVEL** (Default: `"info"`): Console output level (Example: `LOG_LEVEL=debug`)
+  - **TASKMASTER_LOG_LEVEL** (Default: `"info"`): Console output level (Example: `TASKMASTER_LOG_LEVEL=debug`)
   - **DEFAULT_SUBTASKS** (Default: `"3"`): Default subtask count (Example: `DEFAULT_SUBTASKS=5`)
   - **DEFAULT_PRIORITY** (Default: `"medium"`): Default priority (Example: `DEFAULT_PRIORITY=high`)
   - **PROJECT_NAME** (Default: `"MCP SaaS MVP"`): Project name in metadata (Example: `PROJECT_NAME=My Awesome Project`)

assets/scripts_README.md

@@ -31,7 +31,7 @@ Task Master configuration is now managed through two primary methods:
     - Create a `.env` file in your project root for CLI usage.
     - See `assets/env.example` for required key names.
 
-**Important:** Settings like `MODEL`, `MAX_TOKENS`, `TEMPERATURE`, `LOG_LEVEL`, etc., are **no longer set via `.env`**. Use `task-master models --setup` instead.
+**Important:** Settings like `MODEL`, `MAX_TOKENS`, `TEMPERATURE`, `TASKMASTER_LOG_LEVEL`, etc., are **no longer set via `.env`**. Use `task-master models --setup` instead.
 
 ## How It Works
 
@@ -42,7 +42,7 @@ Task Master configuration is now managed through two primary methods:
    - Tasks can have `subtasks` for more detailed implementation steps.
    - Dependencies are displayed with status indicators (✅ for completed, ⏱️ for pending) to easily track progress.
 
-2. **CLI Commands**  
+2. **CLI Commands**
    You can run the commands via:
 
    ```bash
@@ -200,7 +200,7 @@ Notes:
 
 ## Logging
 
-The script supports different logging levels controlled by the `LOG_LEVEL` environment variable:
+The script supports different logging levels controlled by the `TASKMASTER_LOG_LEVEL` environment variable:
 
 - `debug`: Detailed information, typically useful for troubleshooting
 - `info`: Confirmation that things are working as expected (default)

context/chats/max-min-tokens.txt.md

@@ -0,0 +1,368 @@
+We want to refine how Task Master handles AI model token limits to be more precise, by:
+
+1.  Distinguishing between `maxInputTokens` and `maxOutputTokens` in the configuration.
+2.  Dynamically adjusting the `maxOutputTokens` for an API call based on the actual prompt length to stay within the model's total context window (or respecting separate input/output limits if the API and model support that).
+3.  Ensuring that `ai-services-unified.js` uses these more granular limits.
+
+This is a good improvement for both cost control and preventing errors.
+
+Here's a plan:
+
+**Phase 1: Configuration and Core Logic Updates**
+
+1.  **Update `.taskmasterconfig` Structure:**
+    - I'll modify the `models` section in your `.taskmasterconfig`. For each role (`main`, `research`, `fallback`), `maxTokens` will be replaced with `maxInputTokens` and `maxOutputTokens`.
+      - We'll need to decide on reasonable default values for these new fields. We can look at the current `maxTokens` and the model's known limits to make an initial guess.
+2.  **Update `MODEL_MAP` in `ai-services-unified.js`:**
+    - This array already stores cost data. We need to ensure it also stores the _absolute_ maximum input and output tokens for each model listed (e.g., `model_max_input_tokens`, `model_max_output_tokens`). If these fields are not present, they will need to be added. The values in `.taskmasterconfig` will then represent user-defined operational limits, which should ideally be validated against these absolute maximums.
+3.  **Update `config-manager.js`:**
+    - Getter functions like `getParametersForRole` will be updated to fetch `maxInputTokens` and `maxOutputTokens` instead of the singular `maxTokens`.
+    - New getters might be needed if we want to access the model's absolute limits directly from `MODEL_MAP` via `config-manager.js`.
+4.  **Update `ai-services-unified.js` (`_unifiedServiceRunner`):**
+    - **Token Counting:** This is a crucial step. Before an API call, we need to estimate the token count of the combined `systemPrompt` and `userPrompt`.
+      - The Vercel AI SDK or the individual provider SDKs might offer utilities for this. For example, some SDKs expose a `tokenizer` or a way to count tokens for a given string.
+      - If a direct utility isn't available through the Vercel SDK for the specific provider, we might need to use a library like `tiktoken` for OpenAI/Anthropic models or investigate provider-specific tokenization. This could be complex as tokenization varies between models.
+      - For now, let's assume we can get a reasonable estimate.
+    - **Dynamic Output Token Calculation & Validation:**
+      - Retrieve `configured_max_input_tokens` and `configured_max_output_tokens` from `config-manager.js` for the current role.
+      - Retrieve `model_absolute_max_input_tokens` and `model_absolute_max_output_tokens` from `MODEL_MAP`.
+      - Let `prompt_tokens = countTokens(systemPrompt + userPrompt)`.
+      - **Input Validation:** If `prompt_tokens > configured_max_input_tokens` OR `prompt_tokens > model_absolute_max_input_tokens`, throw an error. (We might also consider auto-truncation strategies in the future, but an error is safer to start).
+      - **Output Calculation:** The `max_tokens` parameter sent to the API (which typically refers to the _max number of tokens to generate_) should be `configured_max_output_tokens`. We also need to ensure this doesn't violate the model's absolute output limit.
+      - Some models have a _total_ context window (input + output). If this is the case for a model, the `max_tokens` parameter for the API call might need to be `min(configured_max_output_tokens, model_absolute_total_tokens - prompt_tokens)`. However, many modern APIs handle separate input and output limits, simplifying this. We'll aim to pass `configured_max_output_tokens` as the API's `max_tokens` parameter, assuming the input validation has passed.
+    - **API Call Parameter:** Update the `callParams` to use the correctly determined `max_tokens` for generation (which will be our `configured_max_output_tokens`).
+
+**Phase 2: Error Handling and Refinements**
+
+1.  **Enhanced Error Handling:** Provide clear error messages if prompt tokens exceed input limits, or if API calls fail due to token issues despite our calculations.
+2.  **Validation:** Add validation (perhaps in `config-manager.js` or when `task-master models --setup` is run) to ensure `configured_max_input_tokens` and `configured_max_output_tokens` in `.taskmasterconfig` do not exceed the `model_absolute_max_input_tokens` and `model_absolute_max_output_tokens` from `MODEL_MAP`.
+
+Let's start by inspecting `scripts/modules/config-manager.js` to see how parameters and `MODEL_MAP` are currently handled. This will inform the changes needed for `.taskmasterconfig` and `ai-services-unified.js`.
+
+I will now read `scripts/modules/config-manager.js`.
+
+Okay, I've reviewed `scripts/modules/config-manager.js`. Here's what I've gathered and how it impacts the plan:
+
+- **`MODEL_MAP` Source:** `MODEL_MAP` is loaded from `supported-models.json`. This is excellent, as it means we can update the model capabilities (including new token limits) in a structured JSON file.
+- **Configuration Loading:** `_loadAndValidateConfig` handles loading `.taskmasterconfig` and merging it with `DEFAULTS`.
+- **Getters:** There are specific getters for `maxTokens` for each role (e.g., `getMainMaxTokens`). We will need to update these.
+- **`getParametersForRole`:** This function already exists and is a good candidate for returning the new `maxInputTokens` and `maxOutputTokens`.
+
+Here's the refined plan:
+
+**Phase 1: Configuration and Data Structures**
+
+1.  **Update `supported-models.json`:**
+
+    - For each model entry in `supported-models.json`, we need to add `maxInputTokens` and `maxOutputTokens` fields. These should reflect the _absolute maximums_ for that specific model.
+    - Example entry change:
+
+      ```json
+      // Before
+      {
+          "id": "claude-3-7-sonnet-20250219",
+          "name": "Claude 3.7 Sonnet (Preview)",
+          "context_window": 200000, // This might be total context
+          "cost_per_1m_tokens": { "input": 3, "output": 15, "currency": "USD" }
+      }
+
+      // After (assuming 200k is total, and we make a reasonable split or find official numbers)
+      {
+          "id": "claude-3-7-sonnet-20250219",
+          "name": "Claude 3.7 Sonnet (Preview)",
+          "maxInputTokens": 190000, // Example
+          "maxOutputTokens": 8192,  // Example, often smaller for generation
+          "cost_per_1m_tokens": { "input": 3, "output": 15, "currency": "USD" }
+      }
+      ```
+
+    - I will need to find the official input/output token limits for the models currently in your `MODEL_MAP`. If you have this information handy, it would speed things up. Otherwise, I can make educated guesses or search for them.
+
+2.  **Update `.taskmasterconfig` (Defaults and User File):**
+    - In `scripts/modules/config-manager.js`, modify the `DEFAULTS` object. For each role (`main`, `research`, `fallback`), replace `maxTokens` with:
+      - `maxInputTokens`: A sensible default (e.g., a large portion of the model's capability, but user-configurable).
+      - `maxOutputTokens`: A sensible default for generation (e.g., 4096 or 8192).
+    - You will then need to manually update your existing `.taskmasterconfig` file to reflect this new structure. I can provide the snippet for you to paste.
+3.  **Update `config-manager.js`:**
+    - Modify `getParametersForRole(role, explicitRoot = null)`:
+      - It currently fetches `maxTokens` and `temperature`.
+      - Update it to fetch `maxInputTokens`, `maxOutputTokens`, and `temperature` from the loaded config for the given role.
+    - Remove the role-specific `getMaxTokens` functions (e.g., `getMainMaxTokens`, `getResearchMaxTokens`, `getFallbackMaxTokens`). The `getParametersForRole` will be the central way to get these.
+    - (Optional, for later validation) Consider adding a new function `getModelCapabilities(providerName, modelId)` that reads from `MODEL_MAP` to return the absolute `maxInputTokens` and `maxOutputTokens` for a given model. This would be useful for validating the user's settings in `.taskmasterconfig`.
+
+**Phase 2: Core Logic in `ai-services-unified.js`**
+
+1.  **Token Counting (`_unifiedServiceRunner`):**
+
+    - This is the most complex part. We need a reliable way to count tokens for the prompts.
+      - **Strategy 1 (Ideal):** Leverage Vercel AI SDK. The SDK might provide a way to get a tokenizer for the active model or a utility function. We'll need to investigate its capabilities.
+      - **Strategy 2 (Fallback):** Use a library like `tiktoken` for models compatible with OpenAI's tokenization (many are, including some Anthropic models). For other models, we might need provider-specific tokenizers or make estimations (less ideal).
+      - **Initial Approach:** Let's try to find a Vercel AI SDK utility first. If not, we'll start with `tiktoken` as a common case and acknowledge that other models might need specific handling later.
+    - The function `_unifiedServiceRunner` will call this token counting utility:
+
+      ```javascript
+      // Placeholder for token counting
+      function countTokens(text, modelId /* or providerName */) {
+      	// ... implementation using SDK or library ...
+      	// This is a simplification; actual tokenization is model-specific.
+      	// For now, a rough estimate might be (text.length / 4) as a placeholder
+      	// if a proper tokenizer isn't immediately available.
+      	if (!text) return 0;
+      	return Math.ceil(text.length / 3.5); // Very rough general estimate
+      }
+
+      const promptTokens = countTokens(systemPrompt) + countTokens(prompt);
+      ```
+
+2.  **Dynamic Output Token Calculation & Validation (`_unifiedServiceRunner`):**
+
+    - Fetch parameters using the updated `getParametersForRole` from `config-manager.js`:
+      ```javascript
+      const roleParams = getParametersForRole(
+      	currentRole,
+      	effectiveProjectRoot
+      );
+      // roleParams will now contain { maxInputTokens, maxOutputTokens, temperature }
+      ```
+    - Fetch model's absolute limits from `MODEL_MAP` (we'll need a helper in `config-manager.js` or do it directly in `ai-services-unified.js` for now):
+      ```javascript
+      // Simplified; ideally use a robust getter from config-manager
+      const modelInfo = MODEL_MAP[providerName?.toLowerCase()]?.find(
+      	(m) => m.id === modelId
+      );
+      const modelAbsoluteMaxInput = modelInfo?.maxInputTokens || Infinity;
+      const modelAbsoluteMaxOutput =
+      	modelInfo?.maxOutputTokens || roleParams.maxOutputTokens; // Fallback to configured if not in map
+      ```
+    - **Input Validation:**
+      ```javascript
+      if (promptTokens > roleParams.maxInputTokens) {
+      	throw new Error(
+      		`Prompt (${promptTokens} tokens) exceeds configured max input tokens (${roleParams.maxInputTokens}) for role '${currentRole}'.`
+      	);
+      }
+      if (promptTokens > modelAbsoluteMaxInput) {
+      	throw new Error(
+      		`Prompt (${promptTokens} tokens) exceeds model's absolute max input tokens (${modelAbsoluteMaxInput}) for ${modelId}.`
+      	);
+      }
+      ```
+    - **API Call Parameter (`max_tokens` for generation):**
+      The `max_tokens` parameter passed to the provider's API call should be `roleParams.maxOutputTokens`. We must also ensure this doesn't exceed the model's absolute output capability.
+
+      ```javascript
+      const apiMaxOutputTokens = Math.min(
+      	roleParams.maxOutputTokens,
+      	modelAbsoluteMaxOutput
+      );
+
+      const callParams = {
+      	apiKey,
+      	modelId,
+      	// Use apiMaxOutputTokens for the actual API call
+      	maxTokens: apiMaxOutputTokens, // This usually means max tokens to GENERATE
+      	temperature: roleParams.temperature,
+      	messages,
+      	baseUrl,
+      	...(serviceType === 'generateObject' && { schema, objectName }),
+      	...restApiParams
+      };
+      ```
+
+3.  **Update `logAiUsage`:**
+    - The cost calculation in `logAiUsage` seems correct as it already uses `inputCost` and `outputCost` per 1M tokens. This part should remain compatible.
+
+**Next Steps:**
+
+1.  **Modify `supported-models.json`:** I need the content of this file to propose the new `maxInputTokens` and `maxOutputTokens` fields. Please provide it.
+2.  **Decide on Tokenizer Strategy:** What are your thoughts on the token counting? Are you aware of any utilities within Vercel AI SDK for this, or should we plan to integrate `tiktoken` initially?
+
+Once I have the content of `supported-models.json`, I can draft the changes for it and then proceed to `config-manager.js` and `.taskmasterconfig`.
+
+```json
+        // Before
+        {
+            "id": "claude-3-7-sonnet-20250219",
+            "name": "Claude 3.7 Sonnet (Preview)",
+            "context_window": 200000, // This might be total context
+            "cost_per_1m_tokens": { "input": 3, "output": 15, "currency": "USD" }
+        }
+
+        // After (assuming 200k is total, and we make a reasonable split or find official numbers)
+        {
+            "id": "claude-3-7-sonnet-20250219",
+            "name": "Claude 3.7 Sonnet (Preview)",
+            "maxInputTokens": 190000, // Example
+            "maxOutputTokens": 8192,  // Example, often smaller for generation
+            "cost_per_1m_tokens": { "input": 3, "output": 15, "currency": "USD" }
+        }
+```
+
+```javascript
+// Placeholder for token counting
+function countTokens(text, modelId /* or providerName */) {
+	// ... implementation using SDK or library ...
+	// This is a simplification; actual tokenization is model-specific.
+	// For now, a rough estimate might be (text.length / 4) as a placeholder
+	// if a proper tokenizer isn't immediately available.
+	if (!text) return 0;
+	return Math.ceil(text.length / 3.5); // Very rough general estimate
+}
+
+const promptTokens = countTokens(systemPrompt) + countTokens(prompt);
+```
+
+```javascript
+const roleParams = getParametersForRole(currentRole, effectiveProjectRoot);
+// roleParams will now contain { maxInputTokens, maxOutputTokens, temperature }
+```
+
+```javascript
+// Simplified; ideally use a robust getter from config-manager
+const modelInfo = MODEL_MAP[providerName?.toLowerCase()]?.find(
+	(m) => m.id === modelId
+);
+const modelAbsoluteMaxInput = modelInfo?.maxInputTokens || Infinity;
+const modelAbsoluteMaxOutput =
+	modelInfo?.maxOutputTokens || roleParams.maxOutputTokens; // Fallback to configured if not in map
+```
+
+```javascript
+if (promptTokens > roleParams.maxInputTokens) {
+	throw new Error(
+		`Prompt (${promptTokens} tokens) exceeds configured max input tokens (${roleParams.maxInputTokens}) for role '${currentRole}'.`
+	);
+}
+if (promptTokens > modelAbsoluteMaxInput) {
+	throw new Error(
+		`Prompt (${promptTokens} tokens) exceeds model's absolute max input tokens (${modelAbsoluteMaxInput}) for ${modelId}.`
+	);
+}
+```
+
+```javascript
+const apiMaxOutputTokens = Math.min(
+	roleParams.maxOutputTokens,
+	modelAbsoluteMaxOutput
+);
+
+const callParams = {
+	apiKey,
+	modelId,
+	// Use apiMaxOutputTokens for the actual API call
+	maxTokens: apiMaxOutputTokens, // This usually means max tokens to GENERATE
+	temperature: roleParams.temperature,
+	messages,
+	baseUrl,
+	...(serviceType === 'generateObject' && { schema, objectName }),
+	...restApiParams
+};
+```
+
+Okay, I have the contents of `supported-models.json`.
+
+It seems many models have a `max_tokens` field, which I assume is the _total context window_. Some don't have it, and for others (like OpenAI `gpt-4o`), the listed `max_tokens` (16384) is known to be its _output_ token limit when using the Chat Completions API, while the context window is much larger (128k). This highlights the complexity: `max_tokens` in `supported-models.json` is used inconsistently.
+
+**Revised Plan for `supported-models.json` and Token Definitions:**
+
+To bring clarity, we'll introduce two new fields and ensure their meaning is consistent:
+
+- `contextWindowTokens`: The total number of tokens the model can process (input + output). This would replace the current ambiguous `max_tokens`.
+- `maxOutputTokens`: The maximum number of tokens the model can _generate_ in a single response. This is often smaller than the total context window, especially for larger models.
+
+If a model _only_ specifies a total context window, we'll have to make a reasonable assumption for `maxOutputTokens` (e.g., 4096 or 8192, or a fraction of the total context window). If it only specifies an output token limit (like some OpenAI models in certain API modes), we'll need to find its total context window.
+
+**Updated `supported-models.json` Structure (Example):**
+
+```json
+// For a model like Anthropic Claude 3.7 Sonnet (Preview)
+{
+    "id": "claude-3-7-sonnet-20250219",
+    "swe_score": 0.623,
+    "cost_per_1m_tokens": { "input": 3.0, "output": 15.0 },
+    "allowed_roles": ["main", "fallback"],
+    "contextWindowTokens": 200000, // From Anthropic's documentation
+    "maxOutputTokens": 8192     // Anthropic default, user can override in .taskmasterconfig
+}
+
+// For a model like OpenAI GPT-4o
+{
+    "id": "gpt-4o",
+    "swe_score": 0.332,
+    "cost_per_1m_tokens": { "input": 2.5, "output": 10.0 },
+    "allowed_roles": ["main", "fallback"],
+    "contextWindowTokens": 128000, // Official context window
+    "maxOutputTokens": 16384    // Max output for chat completions is 16k for gpt-4o (used to be 4k/8k for older gpt-4)
+                                // but the model card mentions 128k total. The API call parameter for `max_tokens` sets this output cap.
+}
+```
+
+I will proceed to generate the updated `supported-models.json` content. This will be a significant change. I will make my best effort to find the correct `contextWindowTokens` and `maxOutputTokens` for each model. If official numbers are ambiguous or not readily available, I'll use sensible defaults and add a comment.
+
+**Regarding Tokenizer Strategy:**
+
+- **Vercel AI SDK:** The `ai` package (Vercel AI SDK) itself is a lightweight wrapper. Tokenization is usually handled by the underlying provider-specific SDKs (e.g., `@anthropic-ai/sdk`, `openai`). The Vercel SDK doesn't provide a universal tokenizer.
+- **Provider SDKs:**
+  - `@anthropic-ai/sdk`: Does not expose a public tokenizer directly. Anthropic recommends counting based on their guidelines (roughly 3.5 chars/token for English).
+  - `openai` (for Node.js): The `gpt-3-encoder` or `tiktoken` libraries are commonly used. `tiktoken` is more current.
+- **`tiktoken`:** This is a good general-purpose library for OpenAI models and those that use compatible tokenization (many models fine-tuned from Llama, etc., might be close). It supports various encodings.
+- **Recommendation:**
+  1.  Start by integrating `tiktoken`. We can select the encoding based on the provider or even specific model if needed (e.g., `cl100k_base` for gpt-4, gpt-3.5-turbo, DALL-E models).
+  2.  For providers where `tiktoken` is not accurate (e.g., Google's Gemini, Perplexity if they have proprietary tokenizers), we might have to rely on character-based estimations (e.g., `length / N`) as a rougher guide or investigate if their specific SDKs offer token counting.
+  3.  The Vercel AI SDK's `experimental_streamObject` and other functions _might_ internally handle some aspects of token counting for request preparation, but we need explicit counting for our logic _before_ the call.
+
+For now, let's assume we'll add a `countTokens` utility that uses `tiktoken` for OpenAI and Anthropic (as Anthropic's guidance is character-based but `cl100k_base` is often a decent proxy for their tokenization for rough estimates, though not perfect), and a character-based fallback for others.
+
+I will now generate the proposed changes for `scripts/modules/supported-models.json`. This will be a large diff.
+
+After this, we'll move to:
+
+1.  Updating `.taskmasterconfig` defaults in `config-manager.js` and providing you the snippet for your user file.
+2.  Updating `config-manager.js` getters.
+3.  Implementing the logic in `ai-services-unified.js`.
+
+```json
+// For a model like Anthropic Claude 3.7 Sonnet (Preview)
+{
+    "id": "claude-3-7-sonnet-20250219",
+    "swe_score": 0.623,
+    "cost_per_1m_tokens": { "input": 3.0, "output": 15.0 },
+    "allowed_roles": ["main", "fallback"],
+    "contextWindowTokens": 200000, // From Anthropic's documentation
+    "maxOutputTokens": 8192     // Anthropic default, user can override in .taskmasterconfig
+}
+
+// For a model like OpenAI GPT-4o
+{
+    "id": "gpt-4o",
+    "swe_score": 0.332,
+    "cost_per_1m_tokens": { "input": 2.5, "output": 10.0 },
+    "allowed_roles": ["main", "fallback"],
+    "contextWindowTokens": 128000, // Official context window
+    "maxOutputTokens": 16384    // Max output for chat completions is 16k for gpt-4o (used to be 4k/8k for older gpt-4)
+                                // but the model card mentions 128k total. The API call parameter for `max_tokens` sets this output cap.
+}
+```
+
+```json
+// For a model like Anthropic Claude 3.7 Sonnet (Preview)
+{
+    "id": "claude-3-7-sonnet-20250219",
+    "swe_score": 0.623,
+    "cost_per_1m_tokens": { "input": 3.0, "output": 15.0 },
+    "allowed_roles": ["main", "fallback"],
+    "contextWindowTokens": 200000, // From Anthropic's documentation
+    "maxOutputTokens": 8192     // Anthropic default, user can override in .taskmasterconfig
+}
+
+// For a model like OpenAI GPT-4o
+{
+    "id": "gpt-4o",
+    "swe_score": 0.332,
+    "cost_per_1m_tokens": { "input": 2.5, "output": 10.0 },
+    "allowed_roles": ["main", "fallback"],
+    "contextWindowTokens": 128000, // Official context window
+    "maxOutputTokens": 16384    // Max output for chat completions is 16k for gpt-4o (used to be 4k/8k for older gpt-4)
+                                // but the model card mentions 128k total. The API call parameter for `max_tokens` sets this output cap.
+}
+```

docs/configuration.md

@@ -15,13 +15,15 @@ Taskmaster uses two primary methods for configuration:
       			"provider": "anthropic",
       			"modelId": "claude-3-7-sonnet-20250219",
       			"maxTokens": 64000,
-      			"temperature": 0.2
+      			"temperature": 0.2,
+      			"baseUrl": "https://api.anthropic.com/v1"
       		},
       		"research": {
       			"provider": "perplexity",
       			"modelId": "sonar-pro",
       			"maxTokens": 8700,
-      			"temperature": 0.1
+      			"temperature": 0.1,
+      			"baseUrl": "https://api.perplexity.ai/v1"
       		},
       		"fallback": {
       			"provider": "anthropic",
@@ -56,8 +58,9 @@ Taskmaster uses two primary methods for configuration:
       - `AZURE_OPENAI_API_KEY`: Your Azure OpenAI API key (also requires `AZURE_OPENAI_ENDPOINT`).
       - `OPENROUTER_API_KEY`: Your OpenRouter API key.
       - `XAI_API_KEY`: Your X-AI API key.
-    - **Optional Endpoint Overrides (in .taskmasterconfig):**
+    - **Optional Endpoint Overrides:**
-      - `AZURE_OPENAI_ENDPOINT`: Required if using Azure OpenAI key.
+      - **Per-role `baseUrl` in `.taskmasterconfig`:** You can add a `baseUrl` property to any model role (`main`, `research`, `fallback`) to override the default API endpoint for that provider. If omitted, the provider's standard endpoint is used.
+      - `AZURE_OPENAI_ENDPOINT`: Required if using Azure OpenAI key (can also be set as `baseUrl` for the Azure model role).
       - `OLLAMA_BASE_URL`: Override the default Ollama API URL (Default: `http://localhost:11434/api`).
 
 **Important:** Settings like model ID selections (`main`, `research`, `fallback`), `maxTokens`, `temperature`, `logLevel`, `defaultSubtasks`, `defaultPriority`, and `projectName` are **managed in `.taskmasterconfig`**, not environment variables.

mcp-server/src/core/direct-functions/add-task.js

@@ -94,6 +94,7 @@ export async function addTaskDirect(args, log, context = {}) {
 
 		let manualTaskData = null;
 		let newTaskId;
+		let telemetryData;
 
 		if (isManualCreation) {
 			// Create manual task data object
@@ -109,7 +110,7 @@ export async function addTaskDirect(args, log, context = {}) {
 			);
 
 			// Call the addTask function with manual task data
-			newTaskId = await addTask(
+			const result = await addTask(
 				tasksPath,
 				null, // prompt is null for manual creation
 				taskDependencies,
@@ -117,13 +118,17 @@ export async function addTaskDirect(args, log, context = {}) {
 				{
 					session,
 					mcpLog,
-					projectRoot
+					projectRoot,
+					commandName: 'add-task',
+					outputType: 'mcp'
 				},
 				'json', // outputFormat
 				manualTaskData, // Pass the manual task data
 				false, // research flag is false for manual creation
 				projectRoot // Pass projectRoot
 			);
+			newTaskId = result.newTaskId;
+			telemetryData = result.telemetryData;
 		} else {
 			// AI-driven task creation
 			log.info(
@@ -131,7 +136,7 @@ export async function addTaskDirect(args, log, context = {}) {
 			);
 
 			// Call the addTask function, passing the research flag
-			newTaskId = await addTask(
+			const result = await addTask(
 				tasksPath,
 				prompt, // Use the prompt for AI creation
 				taskDependencies,
@@ -139,12 +144,16 @@ export async function addTaskDirect(args, log, context = {}) {
 				{
 					session,
 					mcpLog,
-					projectRoot
+					projectRoot,
+					commandName: 'add-task',
+					outputType: 'mcp'
 				},
 				'json', // outputFormat
 				null, // manualTaskData is null for AI creation
 				research // Pass the research flag
 			);
+			newTaskId = result.newTaskId;
+			telemetryData = result.telemetryData;
 		}
 
 		// Restore normal logging
@@ -154,7 +163,8 @@ export async function addTaskDirect(args, log, context = {}) {
 			success: true,
 			data: {
 				taskId: newTaskId,
-				message: `Successfully added new task #${newTaskId}`
+				message: `Successfully added new task #${newTaskId}`,
+				telemetryData: telemetryData
 			}
 		};
 	} catch (error) {

mcp-server/src/core/direct-functions/analyze-task-complexity.js

@@ -79,17 +79,19 @@ export async function analyzeTaskComplexityDirect(args, log, context = {}) {
 		}
 
 		let report;
+		let coreResult;
 
 		try {
 			// --- Call Core Function (Pass context separately) ---
 			// Pass coreOptions as the first argument
 			// Pass context object { session, mcpLog } as the second argument
-			report = await analyzeTaskComplexity(
+			coreResult = await analyzeTaskComplexity(coreOptions, {
-				coreOptions, // Pass options object
+				session,
-				{ session, mcpLog: logWrapper } // Pass context object
+				mcpLog: logWrapper,
-				// Removed the explicit 'json' format argument, assuming context handling is sufficient
+				commandName: 'analyze-complexity',
-				// If issues persist, we might need to add an explicit format param to analyzeTaskComplexity
+				outputType: 'mcp'
-			);
+			});
+			report = coreResult.report;
 		} catch (error) {
 			log.error(
 				`Error in analyzeTaskComplexity core function: ${error.message}`
@@ -125,8 +127,11 @@ export async function analyzeTaskComplexityDirect(args, log, context = {}) {
 			};
 		}
 
-		// Added a check to ensure report is defined before accessing its properties
+		if (
-		if (!report || typeof report !== 'object') {
+			!coreResult ||
+			!coreResult.report ||
+			typeof coreResult.report !== 'object'
+		) {
 			log.error(
 				'Core analysis function returned an invalid or undefined response.'
 			);
@@ -141,8 +146,8 @@ export async function analyzeTaskComplexityDirect(args, log, context = {}) {
 
 		try {
 			// Ensure complexityAnalysis exists and is an array
-			const analysisArray = Array.isArray(report.complexityAnalysis)
+			const analysisArray = Array.isArray(coreResult.report.complexityAnalysis)
-				? report.complexityAnalysis
+				? coreResult.report.complexityAnalysis
 				: [];
 
 			// Count tasks by complexity (remains the same)
@@ -159,15 +164,16 @@ export async function analyzeTaskComplexityDirect(args, log, context = {}) {
 			return {
 				success: true,
 				data: {
-					message: `Task complexity analysis complete. Report saved to ${outputPath}`, // Use outputPath from args
+					message: `Task complexity analysis complete. Report saved to ${outputPath}`,
-					reportPath: outputPath, // Use outputPath from args
+					reportPath: outputPath,
 					reportSummary: {
 						taskCount: analysisArray.length,
 						highComplexityTasks,
 						mediumComplexityTasks,
 						lowComplexityTasks
 					},
-					fullReport: report // Now includes the full report
+					fullReport: coreResult.report,
+					telemetryData: coreResult.telemetryData
 				}
 			};
 		} catch (parseError) {

mcp-server/src/core/direct-functions/complexity-report.js

@@ -8,7 +8,6 @@ import {
 	enableSilentMode,
 	disableSilentMode
 } from '../../../../scripts/modules/utils.js';
-import { getCachedOrExecute } from '../../tools/utils.js';
 
 /**
  * Direct function wrapper for displaying the complexity report with error handling and caching.
@@ -86,30 +85,20 @@ export async function complexityReportDirect(args, log) {
 
 		// Use the caching utility
 		try {
-			const result = await getCachedOrExecute({
+			const result = await coreActionFn();
-				cacheKey,
+			log.info('complexityReportDirect completed');
-				actionFn: coreActionFn,
+			return result;
-				log
-			});
-			log.info(
-				`complexityReportDirect completed. From cache: ${result.fromCache}`
-			);
-			return result; // Returns { success, data/error, fromCache }
 		} catch (error) {
-			// Catch unexpected errors from getCachedOrExecute itself
 			// Ensure silent mode is disabled
 			disableSilentMode();
 
-			log.error(
+			log.error(`Unexpected error during complexityReport: ${error.message}`);
-				`Unexpected error during getCachedOrExecute for complexityReport: ${error.message}`
-			);
 			return {
 				success: false,
 				error: {
 					code: 'UNEXPECTED_ERROR',
 					message: error.message
-				},
+				}
-				fromCache: false
 			};
 		}
 	} catch (error) {

mcp-server/src/core/direct-functions/expand-all-tasks.js

@@ -63,12 +63,18 @@ export async function expandAllTasksDirect(args, log, context = {}) {
 			{ session, mcpLog, projectRoot }
 		);
 
-		// Core function now returns a summary object
+		// Core function now returns a summary object including the *aggregated* telemetryData
 		return {
 			success: true,
 			data: {
 				message: `Expand all operation completed. Expanded: ${result.expandedCount}, Failed: ${result.failedCount}, Skipped: ${result.skippedCount}`,
-				details: result // Include the full result details
+				details: {
+					expandedCount: result.expandedCount,
+					failedCount: result.failedCount,
+					skippedCount: result.skippedCount,
+					tasksToExpand: result.tasksToExpand
+				},
+				telemetryData: result.telemetryData // Pass the aggregated object
 			}
 		};
 	} catch (error) {

mcp-server/src/core/direct-functions/expand-task.js

@@ -193,13 +193,19 @@ export async function expandTaskDirect(args, log, context = {}) {
 			if (!wasSilent) enableSilentMode();
 
 			// Call the core expandTask function with the wrapped logger and projectRoot
-			const updatedTaskResult = await expandTask(
+			const coreResult = await expandTask(
 				tasksPath,
 				taskId,
 				numSubtasks,
 				useResearch,
 				additionalContext,
-				{ mcpLog, session, projectRoot },
+				{
+					mcpLog,
+					session,
+					projectRoot,
+					commandName: 'expand-task',
+					outputType: 'mcp'
+				},
 				forceFlag
 			);
 
@@ -215,16 +221,17 @@ export async function expandTaskDirect(args, log, context = {}) {
 				? updatedTask.subtasks.length - subtasksCountBefore
 				: 0;
 
-			// Return the result
+			// Return the result, including telemetryData
 			log.info(
 				`Successfully expanded task ${taskId} with ${subtasksAdded} new subtasks`
 			);
 			return {
 				success: true,
 				data: {
-					task: updatedTask,
+					task: coreResult.task,
 					subtasksAdded,
-					hasExistingSubtasks
+					hasExistingSubtasks,
+					telemetryData: coreResult.telemetryData
 				},
 				fromCache: false
 			};

mcp-server/src/core/direct-functions/list-tasks.js

@@ -4,7 +4,6 @@
  */
 
 import { listTasks } from '../../../../scripts/modules/task-manager.js';
-import { getCachedOrExecute } from '../../tools/utils.js';
 import {
 	enableSilentMode,
 	disableSilentMode
@@ -19,7 +18,7 @@ import {
  */
 export async function listTasksDirect(args, log) {
 	// Destructure the explicit tasksJsonPath from args
-	const { tasksJsonPath, status, withSubtasks } = args;
+	const { tasksJsonPath, reportPath, status, withSubtasks } = args;
 
 	if (!tasksJsonPath) {
 		log.error('listTasksDirect called without tasksJsonPath');
@@ -36,7 +35,6 @@ export async function listTasksDirect(args, log) {
 	// Use the explicit tasksJsonPath for cache key
 	const statusFilter = status || 'all';
 	const withSubtasksFilter = withSubtasks || false;
-	const cacheKey = `listTasks:${tasksJsonPath}:${statusFilter}:${withSubtasksFilter}`;
 
 	// Define the action function to be executed on cache miss
 	const coreListTasksAction = async () => {
@@ -51,6 +49,7 @@ export async function listTasksDirect(args, log) {
 			const resultData = listTasks(
 				tasksJsonPath,
 				statusFilter,
+				reportPath,
 				withSubtasksFilter,
 				'json'
 			);
@@ -65,6 +64,7 @@ export async function listTasksDirect(args, log) {
 					}
 				};
 			}
+
 			log.info(
 				`Core listTasks function retrieved ${resultData.tasks.length} tasks`
 			);
@@ -88,25 +88,19 @@ export async function listTasksDirect(args, log) {
 		}
 	};
 
-	// Use the caching utility
 	try {
-		const result = await getCachedOrExecute({
+		const result = await coreListTasksAction();
-			cacheKey,
+		log.info('listTasksDirect completed');
-			actionFn: coreListTasksAction,
+		return result;
-			log
-		});
-		log.info(`listTasksDirect completed. From cache: ${result.fromCache}`);
-		return result; // Returns { success, data/error, fromCache }
 	} catch (error) {
-		// Catch unexpected errors from getCachedOrExecute itself (though unlikely)
+		log.error(`Unexpected error during listTasks: ${error.message}`);
-		log.error(
-			`Unexpected error during getCachedOrExecute for listTasks: ${error.message}`
-		);
 		console.error(error.stack);
 		return {
 			success: false,
-			error: { code: 'CACHE_UTIL_ERROR', message: error.message },
+			error: {
-			fromCache: false
+				code: 'UNEXPECTED_ERROR',
+				message: error.message
+			}
 		};
 	}
 }

mcp-server/src/core/direct-functions/next-task.js

@@ -4,8 +4,10 @@
  */
 
 import { findNextTask } from '../../../../scripts/modules/task-manager.js';
-import { readJSON } from '../../../../scripts/modules/utils.js';
+import {
-import { getCachedOrExecute } from '../../tools/utils.js';
+	readJSON,
+	readComplexityReport
+} from '../../../../scripts/modules/utils.js';
 import {
 	enableSilentMode,
 	disableSilentMode
@@ -21,7 +23,7 @@ import {
  */
 export async function nextTaskDirect(args, log) {
 	// Destructure expected args
-	const { tasksJsonPath } = args;
+	const { tasksJsonPath, reportPath } = args;
 
 	if (!tasksJsonPath) {
 		log.error('nextTaskDirect called without tasksJsonPath');
@@ -35,9 +37,6 @@ export async function nextTaskDirect(args, log) {
 		};
 	}
 
-	// Generate cache key using the provided task path
-	const cacheKey = `nextTask:${tasksJsonPath}`;
-
 	// Define the action function to be executed on cache miss
 	const coreNextTaskAction = async () => {
 		try {
@@ -59,8 +58,11 @@ export async function nextTaskDirect(args, log) {
 				};
 			}
 
+			// Read the complexity report
+			const complexityReport = readComplexityReport(reportPath);
+
 			// Find the next task
-			const nextTask = findNextTask(data.tasks);
+			const nextTask = findNextTask(data.tasks, complexityReport);
 
 			if (!nextTask) {
 				log.info(
@@ -118,18 +120,11 @@ export async function nextTaskDirect(args, log) {
 
 	// Use the caching utility
 	try {
-		const result = await getCachedOrExecute({
+		const result = await coreNextTaskAction();
-			cacheKey,
+		log.info(`nextTaskDirect completed.`);
-			actionFn: coreNextTaskAction,
+		return result;
-			log
-		});
-		log.info(`nextTaskDirect completed. From cache: ${result.fromCache}`);
-		return result; // Returns { success, data/error, fromCache }
 	} catch (error) {
-		// Catch unexpected errors from getCachedOrExecute itself
+		log.error(`Unexpected error during nextTask: ${error.message}`);
-		log.error(
-			`Unexpected error during getCachedOrExecute for nextTask: ${error.message}`
-		);
 		return {
 			success: false,
 			error: {

mcp-server/src/core/direct-functions/parse-prd.js

@@ -105,11 +105,9 @@ export async function parsePRDDirect(args, log, context = {}) {
 		}
 	}
 
-	const useForce = force === true;
+	if (append) {
-	const useAppend = append === true;
-	if (useAppend) {
 		logWrapper.info('Append mode enabled.');
-		if (useForce) {
+		if (force) {
 			logWrapper.warn(
 				'Both --force and --append flags were provided. --force takes precedence; append mode will be ignored.'
 			);
@@ -117,7 +115,7 @@ export async function parsePRDDirect(args, log, context = {}) {
 	}
 
 	logWrapper.info(
-		`Parsing PRD via direct function. Input: ${inputPath}, Output: ${outputPath}, NumTasks: ${numTasks}, Force: ${useForce}, Append: ${useAppend}, ProjectRoot: ${projectRoot}`
+		`Parsing PRD via direct function. Input: ${inputPath}, Output: ${outputPath}, NumTasks: ${numTasks}, Force: ${force}, Append: ${append}, ProjectRoot: ${projectRoot}`
 	);
 
 	const wasSilent = isSilentMode();
@@ -131,21 +129,28 @@ export async function parsePRDDirect(args, log, context = {}) {
 			inputPath,
 			outputPath,
 			numTasks,
-			{ session, mcpLog: logWrapper, projectRoot, useForce, useAppend },
+			{
+				session,
+				mcpLog: logWrapper,
+				projectRoot,
+				force,
+				append,
+				commandName: 'parse-prd',
+				outputType: 'mcp'
+			},
 			'json'
 		);
 
-		// parsePRD returns { success: true, tasks: processedTasks } on success
+		// Adjust check for the new return structure
-		if (result && result.success && Array.isArray(result.tasks)) {
+		if (result && result.success) {
-			logWrapper.success(
+			const successMsg = `Successfully parsed PRD and generated tasks in ${result.tasksPath}`;
-				`Successfully parsed PRD. Generated ${result.tasks.length} tasks.`
+			logWrapper.success(successMsg);
-			);
 			return {
 				success: true,
 				data: {
-					message: `Successfully parsed PRD and generated ${result.tasks.length} tasks.`,
+					message: successMsg,
-					outputPath: outputPath,
+					outputPath: result.tasksPath,
-					taskCount: result.tasks.length
+					telemetryData: result.telemetryData
 				}
 			};
 		} else {

mcp-server/src/core/direct-functions/show-task.js

@@ -3,11 +3,10 @@
  * Direct function implementation for showing task details
  */
 
-import { findTaskById, readJSON } from '../../../../scripts/modules/utils.js';
-import { getCachedOrExecute } from '../../tools/utils.js';
 import {
-	enableSilentMode,
+	findTaskById,
-	disableSilentMode
+	readComplexityReport,
+	readJSON
 } from '../../../../scripts/modules/utils.js';
 import { findTasksJsonPath } from '../utils/path-utils.js';
 
@@ -17,6 +16,7 @@ import { findTasksJsonPath } from '../utils/path-utils.js';
  * @param {Object} args - Command arguments.
  * @param {string} args.id - Task ID to show.
  * @param {string} [args.file] - Optional path to the tasks file (passed to findTasksJsonPath).
+ * @param {string} args.reportPath - Explicit path to the complexity report file.
  * @param {string} [args.status] - Optional status to filter subtasks by.
  * @param {string} args.projectRoot - Absolute path to the project root directory (already normalized by tool).
  * @param {Object} log - Logger object.
@@ -27,7 +27,7 @@ export async function showTaskDirect(args, log) {
 	// Destructure session from context if needed later, otherwise ignore
 	// const { session } = context;
 	// Destructure projectRoot and other args. projectRoot is assumed normalized.
-	const { id, file, status, projectRoot } = args;
+	const { id, file, reportPath, status, projectRoot } = args;
 
 	log.info(
 		`Showing task direct function. ID: ${id}, File: ${file}, Status Filter: ${status}, ProjectRoot: ${projectRoot}`
@@ -64,9 +64,12 @@ export async function showTaskDirect(args, log) {
 			};
 		}
 
+		const complexityReport = readComplexityReport(reportPath);
+
 		const { task, originalSubtaskCount } = findTaskById(
 			tasksData.tasks,
 			id,
+			complexityReport,
 			status
 		);
 

mcp-server/src/core/direct-functions/update-subtask-by-id.js

@@ -108,18 +108,24 @@ export async function updateSubtaskByIdDirect(args, log, context = {}) {
 
 		try {
 			// Execute core updateSubtaskById function
-			const updatedSubtask = await updateSubtaskById(
+			const coreResult = await updateSubtaskById(
 				tasksPath,
 				subtaskIdStr,
 				prompt,
 				useResearch,
-				{ mcpLog: logWrapper, session, projectRoot },
+				{
+					mcpLog: logWrapper,
+					session,
+					projectRoot,
+					commandName: 'update-subtask',
+					outputType: 'mcp'
+				},
 				'json'
 			);
 
-			if (updatedSubtask === null) {
+			if (!coreResult || coreResult.updatedSubtask === null) {
 				const message = `Subtask ${id} or its parent task not found.`;
-				logWrapper.error(message); // Log as error since it couldn't be found
+				logWrapper.error(message);
 				return {
 					success: false,
 					error: { code: 'SUBTASK_NOT_FOUND', message: message },
@@ -136,9 +142,10 @@ export async function updateSubtaskByIdDirect(args, log, context = {}) {
 					message: `Successfully updated subtask with ID ${subtaskIdStr}`,
 					subtaskId: subtaskIdStr,
 					parentId: subtaskIdStr.split('.')[0],
-					subtask: updatedSubtask,
+					subtask: coreResult.updatedSubtask,
 					tasksPath,
-					useResearch
+					useResearch,
+					telemetryData: coreResult.telemetryData
 				},
 				fromCache: false
 			};

mcp-server/src/core/direct-functions/update-task-by-id.js

@@ -110,7 +110,7 @@ export async function updateTaskByIdDirect(args, log, context = {}) {
 
 		try {
 			// Execute core updateTaskById function with proper parameters
-			const updatedTask = await updateTaskById(
+			const coreResult = await updateTaskById(
 				tasksPath,
 				taskId,
 				prompt,
@@ -118,19 +118,26 @@ export async function updateTaskByIdDirect(args, log, context = {}) {
 				{
 					mcpLog: logWrapper,
 					session,
-					projectRoot
+					projectRoot,
+					commandName: 'update-task',
+					outputType: 'mcp'
 				},
 				'json'
 			);
 
-			// Check if the core function indicated the task wasn't updated (e.g., status was 'done')
+			// Check if the core function returned null or an object without success
-			if (updatedTask === null) {
+			if (!coreResult || coreResult.updatedTask === null) {
 				// Core function logs the reason, just return success with info
 				const message = `Task ${taskId} was not updated (likely already completed).`;
 				logWrapper.info(message);
 				return {
 					success: true,
-					data: { message: message, taskId: taskId, updated: false },
+					data: {
+						message: message,
+						taskId: taskId,
+						updated: false,
+						telemetryData: coreResult?.telemetryData
+					},
 					fromCache: false
 				};
 			}
@@ -146,7 +153,8 @@ export async function updateTaskByIdDirect(args, log, context = {}) {
 					tasksPath: tasksPath,
 					useResearch: useResearch,
 					updated: true,
-					updatedTask: updatedTask
+					updatedTask: coreResult.updatedTask,
+					telemetryData: coreResult.telemetryData
 				},
 				fromCache: false
 			};

mcp-server/src/core/direct-functions/update-tasks.js

@@ -6,6 +6,10 @@
 import path from 'path';
 import { updateTasks } from '../../../../scripts/modules/task-manager.js';
 import { createLogWrapper } from '../../tools/utils.js';
+import {
+	enableSilentMode,
+	disableSilentMode
+} from '../../../../scripts/modules/utils.js';
 
 /**
  * Direct function wrapper for updating tasks based on new context.
@@ -81,7 +85,6 @@ export async function updateTasksDirect(args, log, context = {}) {
 			'json'
 		);
 
-		// updateTasks returns { success: true, updatedTasks: [...] } on success
 		if (result && result.success && Array.isArray(result.updatedTasks)) {
 			logWrapper.success(
 				`Successfully updated ${result.updatedTasks.length} tasks.`
@@ -91,7 +94,8 @@ export async function updateTasksDirect(args, log, context = {}) {
 				data: {
 					message: `Successfully updated ${result.updatedTasks.length} tasks.`,
 					tasksFile,
-					updatedCount: result.updatedTasks.length
+					updatedCount: result.updatedTasks.length,
+					telemetryData: result.telemetryData
 				}
 			};
 		} else {

mcp-server/src/core/utils/path-utils.js

@@ -339,6 +339,49 @@ export function findPRDDocumentPath(projectRoot, explicitPath, log) {
 	return null;
 }
 
+export function findComplexityReportPath(projectRoot, explicitPath, log) {
+	// If explicit path is provided, check if it exists
+	if (explicitPath) {
+		const fullPath = path.isAbsolute(explicitPath)
+			? explicitPath
+			: path.resolve(projectRoot, explicitPath);
+
+		if (fs.existsSync(fullPath)) {
+			log.info(`Using provided PRD document path: ${fullPath}`);
+			return fullPath;
+		} else {
+			log.warn(
+				`Provided PRD document path not found: ${fullPath}, will search for alternatives`
+			);
+		}
+	}
+
+	// Common locations and file patterns for PRD documents
+	const commonLocations = [
+		'', // Project root
+		'scripts/'
+	];
+
+	const commonFileNames = [
+		'complexity-report.json',
+		'task-complexity-report.json'
+	];
+
+	// Check all possible combinations
+	for (const location of commonLocations) {
+		for (const fileName of commonFileNames) {
+			const potentialPath = path.join(projectRoot, location, fileName);
+			if (fs.existsSync(potentialPath)) {
+				log.info(`Found PRD document at: ${potentialPath}`);
+				return potentialPath;
+			}
+		}
+	}
+
+	log.warn(`No PRD document found in common locations within ${projectRoot}`);
+	return null;
+}
+
 /**
  * Resolves the tasks output directory path
  * @param {string} projectRoot - The project root directory

mcp-server/src/tools/get-task.js

@@ -10,7 +10,10 @@ import {
 	withNormalizedProjectRoot
 } from './utils.js';
 import { showTaskDirect } from '../core/task-master-core.js';
-import { findTasksJsonPath } from '../core/utils/path-utils.js';
+import {
+	findTasksJsonPath,
+	findComplexityReportPath
+} from '../core/utils/path-utils.js';
 
 /**
  * Custom processor function that removes allTasks from the response
@@ -50,6 +53,12 @@ export function registerShowTaskTool(server) {
 				.string()
 				.optional()
 				.describe('Path to the tasks file relative to project root'),
+			complexityReport: z
+				.string()
+				.optional()
+				.describe(
+					'Path to the complexity report file (relative to project root or absolute)'
+				),
 			projectRoot: z
 				.string()
 				.optional()
@@ -81,9 +90,22 @@ export function registerShowTaskTool(server) {
 				}
 
 				// Call the direct function, passing the normalized projectRoot
+				// Resolve the path to complexity report
+				let complexityReportPath;
+				try {
+					complexityReportPath = findComplexityReportPath(
+						projectRoot,
+						args.complexityReport,
+						log
+					);
+				} catch (error) {
+					log.error(`Error finding complexity report: ${error.message}`);
+				}
 				const result = await showTaskDirect(
 					{
 						tasksJsonPath: tasksJsonPath,
+						reportPath: complexityReportPath,
+						// Pass other relevant args
 						id: id,
 						status: status,
 						projectRoot: projectRoot

mcp-server/src/tools/get-tasks.js

@@ -10,7 +10,10 @@ import {
 	withNormalizedProjectRoot
 } from './utils.js';
 import { listTasksDirect } from '../core/task-master-core.js';
-import { findTasksJsonPath } from '../core/utils/path-utils.js';
+import {
+	findTasksJsonPath,
+	findComplexityReportPath
+} from '../core/utils/path-utils.js';
 
 /**
  * Register the getTasks tool with the MCP server
@@ -38,6 +41,12 @@ export function registerListTasksTool(server) {
 				.describe(
 					'Path to the tasks file (relative to project root or absolute)'
 				),
+			complexityReport: z
+				.string()
+				.optional()
+				.describe(
+					'Path to the complexity report file (relative to project root or absolute)'
+				),
 			projectRoot: z
 				.string()
 				.describe('The directory of the project. Must be an absolute path.')
@@ -60,11 +69,23 @@ export function registerListTasksTool(server) {
 					);
 				}
 
+				// Resolve the path to complexity report
+				let complexityReportPath;
+				try {
+					complexityReportPath = findComplexityReportPath(
+						args.projectRoot,
+						args.complexityReport,
+						log
+					);
+				} catch (error) {
+					log.error(`Error finding complexity report: ${error.message}`);
+				}
 				const result = await listTasksDirect(
 					{
 						tasksJsonPath: tasksJsonPath,
 						status: args.status,
-						withSubtasks: args.withSubtasks
+						withSubtasks: args.withSubtasks,
+						reportPath: complexityReportPath
 					},
 					log
 				);

mcp-server/src/tools/next-task.js

@@ -10,7 +10,10 @@ import {
 	withNormalizedProjectRoot
 } from './utils.js';
 import { nextTaskDirect } from '../core/task-master-core.js';
-import { findTasksJsonPath } from '../core/utils/path-utils.js';
+import {
+	findTasksJsonPath,
+	findComplexityReportPath
+} from '../core/utils/path-utils.js';
 
 /**
  * Register the next-task tool with the MCP server
@@ -23,6 +26,12 @@ export function registerNextTaskTool(server) {
 			'Find the next task to work on based on dependencies and status',
 		parameters: z.object({
 			file: z.string().optional().describe('Absolute path to the tasks file'),
+			complexityReport: z
+				.string()
+				.optional()
+				.describe(
+					'Path to the complexity report file (relative to project root or absolute)'
+				),
 			projectRoot: z
 				.string()
 				.describe('The directory of the project. Must be an absolute path.')
@@ -45,9 +54,21 @@ export function registerNextTaskTool(server) {
 					);
 				}
 
+				// Resolve the path to complexity report
+				let complexityReportPath;
+				try {
+					complexityReportPath = findComplexityReportPath(
+						args.projectRoot,
+						args.complexityReport,
+						log
+					);
+				} catch (error) {
+					log.error(`Error finding complexity report: ${error.message}`);
+				}
 				const result = await nextTaskDirect(
 					{
-						tasksJsonPath: tasksJsonPath
+						tasksJsonPath: tasksJsonPath,
+						reportPath: complexityReportPath
 					},
 					log
 				);

mcp-server/src/tools/set-task-status.js

@@ -11,6 +11,7 @@ import {
 } from './utils.js';
 import { setTaskStatusDirect } from '../core/task-master-core.js';
 import { findTasksJsonPath } from '../core/utils/path-utils.js';
+import { TASK_STATUS_OPTIONS } from '../../../src/constants/task-status.js';
 
 /**
  * Register the setTaskStatus tool with the MCP server
@@ -27,7 +28,7 @@ export function registerSetTaskStatusTool(server) {
 					"Task ID or subtask ID (e.g., '15', '15.2'). Can be comma-separated to update multiple tasks/subtasks at once."
 				),
 			status: z
-				.string()
+				.enum(TASK_STATUS_OPTIONS)
 				.describe(
 					"New status to set (e.g., 'pending', 'done', 'in-progress', 'review', 'deferred', 'cancelled'."
 				),

package-lock.json

@@ -1,12 +1,12 @@
 {
 	"name": "task-master-ai",
-	"version": "0.13.2-rc.1",
+	"version": "0.13.2",
 	"lockfileVersion": 3,
 	"requires": true,
 	"packages": {
 		"": {
 			"name": "task-master-ai",
-			"version": "0.13.2-rc.1",
+			"version": "0.13.2",
 			"license": "MIT WITH Commons-Clause",
 			"dependencies": {
 				"@ai-sdk/anthropic": "^1.2.10",

package.json

@@ -1,6 +1,6 @@
 {
 	"name": "task-master-ai",
-	"version": "0.13.2-rc.1",
+	"version": "0.14.0-rc.0",
 	"description": "A task management system for ambitious AI-driven development that doesn't overwhelm and confuse Cursor.",
 	"main": "index.js",
 	"type": "module",

scripts/README.md

@@ -32,7 +32,7 @@ The script can be configured through environment variables in a `.env` file at t
 - `PERPLEXITY_API_KEY`: Your Perplexity API key for research-backed subtask generation
 - `PERPLEXITY_MODEL`: Specify which Perplexity model to use (default: "sonar-medium-online")
 - `DEBUG`: Enable debug logging (default: false)
-- `LOG_LEVEL`: Log level - debug, info, warn, error (default: info)
+- `TASKMASTER_LOG_LEVEL`: Log level - debug, info, warn, error (default: info)
 - `DEFAULT_SUBTASKS`: Default number of subtasks when expanding (default: 3)
 - `DEFAULT_PRIORITY`: Default priority for generated tasks (default: medium)
 - `PROJECT_NAME`: Override default project name in tasks.json
@@ -47,7 +47,7 @@ The script can be configured through environment variables in a `.env` file at t
    - Tasks can have `subtasks` for more detailed implementation steps.
    - Dependencies are displayed with status indicators (✅ for completed, ⏱️ for pending) to easily track progress.
 
-2. **Script Commands**  
+2. **Script Commands**
    You can run the script via:
 
    ```bash
@@ -225,7 +225,7 @@ To use the Perplexity integration:
 
 ## Logging
 
-The script supports different logging levels controlled by the `LOG_LEVEL` environment variable:
+The script supports different logging levels controlled by the `TASKMASTER_LOG_LEVEL` environment variable:
 
 - `debug`: Detailed information, typically useful for troubleshooting
 - `info`: Confirmation that things are working as expected (default)

scripts/init.js

@@ -38,10 +38,10 @@ const LOG_LEVELS = {
 	success: 4
 };
 
-// Get log level from environment or default to info
+// Determine log level from environment variable or default to 'info'
-const LOG_LEVEL = process.env.LOG_LEVEL
+const LOG_LEVEL = process.env.TASKMASTER_LOG_LEVEL
-	? LOG_LEVELS[process.env.LOG_LEVEL.toLowerCase()]
+	? LOG_LEVELS[process.env.TASKMASTER_LOG_LEVEL.toLowerCase()]
-	: LOG_LEVELS.info;
+	: LOG_LEVELS.info; // Default to info
 
 // Create a color gradient for the banner
 const coolGradient = gradient(['#00b4d8', '#0077b6', '#03045e']);

scripts/modules/ai-services-unified.js

@@ -14,9 +14,13 @@ import {
 	getResearchModelId,
 	getFallbackProvider,
 	getFallbackModelId,
-	getParametersForRole
+	getParametersForRole,
+	getUserId,
+	MODEL_MAP,
+	getDebugFlag,
+	getBaseUrlForRole
 } from './config-manager.js';
-import { log, resolveEnvVariable, findProjectRoot } from './utils.js';
+import { log, resolveEnvVariable, isSilentMode } from './utils.js';
 
 import * as anthropic from '../../src/ai-providers/anthropic.js';
 import * as perplexity from '../../src/ai-providers/perplexity.js';
@@ -26,6 +30,36 @@ import * as xai from '../../src/ai-providers/xai.js';
 import * as openrouter from '../../src/ai-providers/openrouter.js';
 // TODO: Import other provider modules when implemented (ollama, etc.)
 
+// Helper function to get cost for a specific model
+function _getCostForModel(providerName, modelId) {
+	if (!MODEL_MAP || !MODEL_MAP[providerName]) {
+		log(
+			'warn',
+			`Provider "${providerName}" not found in MODEL_MAP. Cannot determine cost for model ${modelId}.`
+		);
+		return { inputCost: 0, outputCost: 0, currency: 'USD' }; // Default to zero cost
+	}
+
+	const modelData = MODEL_MAP[providerName].find((m) => m.id === modelId);
+
+	if (!modelData || !modelData.cost_per_1m_tokens) {
+		log(
+			'debug',
+			`Cost data not found for model "${modelId}" under provider "${providerName}". Assuming zero cost.`
+		);
+		return { inputCost: 0, outputCost: 0, currency: 'USD' }; // Default to zero cost
+	}
+
+	// Ensure currency is part of the returned object, defaulting if not present
+	const currency = modelData.cost_per_1m_tokens.currency || 'USD';
+
+	return {
+		inputCost: modelData.cost_per_1m_tokens.input || 0,
+		outputCost: modelData.cost_per_1m_tokens.output || 0,
+		currency: currency
+	};
+}
+
 // --- Provider Function Map ---
 // Maps provider names (lowercase) to their respective service functions
 const PROVIDER_FUNCTIONS = {
@@ -196,18 +230,22 @@ async function _attemptProviderCallWithRetries(
 
 	while (retries <= MAX_RETRIES) {
 		try {
-			log(
+			if (getDebugFlag()) {
-				'info',
+				log(
-				`Attempt ${retries + 1}/${MAX_RETRIES + 1} calling ${fnName} (Provider: ${providerName}, Model: ${modelId}, Role: ${attemptRole})`
+					'info',
-			);
+					`Attempt ${retries + 1}/${MAX_RETRIES + 1} calling ${fnName} (Provider: ${providerName}, Model: ${modelId}, Role: ${attemptRole})`
+				);
+			}
 
 			// Call the specific provider function directly
 			const result = await providerApiFn(callParams);
 
-			log(
+			if (getDebugFlag()) {
-				'info',
+				log(
-				`${fnName} succeeded for role ${attemptRole} (Provider: ${providerName}) on attempt ${retries + 1}`
+					'info',
-			);
+					`${fnName} succeeded for role ${attemptRole} (Provider: ${providerName}) on attempt ${retries + 1}`
+				);
+			}
 			return result;
 		} catch (error) {
 			log(
@@ -220,13 +258,13 @@ async function _attemptProviderCallWithRetries(
 				const delay = INITIAL_RETRY_DELAY_MS * Math.pow(2, retries - 1);
 				log(
 					'info',
-					`Retryable error detected. Retrying in ${delay / 1000}s...`
+					`Something went wrong on the provider side. Retrying in ${delay / 1000}s...`
 				);
 				await new Promise((resolve) => setTimeout(resolve, delay));
 			} else {
 				log(
 					'error',
-					`Non-retryable error or max retries reached for role ${attemptRole} (${fnName} / ${providerName}).`
+					`Something went wrong on the provider side. Max retries reached for role ${attemptRole} (${fnName} / ${providerName}).`
 				);
 				throw error;
 			}
@@ -242,7 +280,15 @@ async function _attemptProviderCallWithRetries(
  * Base logic for unified service functions.
  * @param {string} serviceType - Type of service ('generateText', 'streamText', 'generateObject').
  * @param {object} params - Original parameters passed to the service function.
+ * @param {string} params.role - The initial client role.
+ * @param {object} [params.session=null] - Optional MCP session object.
  * @param {string} [params.projectRoot] - Optional project root path.
+ * @param {string} params.commandName - Name of the command invoking the service.
+ * @param {string} params.outputType - 'cli' or 'mcp'.
+ * @param {string} [params.systemPrompt] - Optional system prompt.
+ * @param {string} [params.prompt] - The prompt for the AI.
+ * @param {string} [params.schema] - The Zod schema for the expected object.
+ * @param {string} [params.objectName] - Name for object/tool.
  * @returns {Promise<any>} Result from the underlying provider call.
  */
 async function _unifiedServiceRunner(serviceType, params) {
@@ -254,15 +300,25 @@ async function _unifiedServiceRunner(serviceType, params) {
 		prompt,
 		schema,
 		objectName,
+		commandName,
+		outputType,
 		...restApiParams
 	} = params;
-	log('info', `${serviceType}Service called`, {
+	if (getDebugFlag()) {
-		role: initialRole,
+		log('info', `${serviceType}Service called`, {
-		projectRoot
+			role: initialRole,
-	});
+			commandName,
+			outputType,
+			projectRoot
+		});
+	}
 
-	// Determine the effective project root (passed in or detected)
+	// Determine the effective project root (passed in or detected if needed by config getters)
-	const effectiveProjectRoot = projectRoot || findProjectRoot();
+	const { findProjectRoot: detectProjectRoot } = await import('./utils.js'); // Dynamically import if needed
+	const effectiveProjectRoot = projectRoot || detectProjectRoot();
+
+	// Get userId from config - ensure effectiveProjectRoot is passed
+	const userId = getUserId(effectiveProjectRoot);
 
 	let sequence;
 	if (initialRole === 'main') {
@@ -284,7 +340,15 @@ async function _unifiedServiceRunner(serviceType, params) {
 		'AI service call failed for all configured roles.';
 
 	for (const currentRole of sequence) {
-		let providerName, modelId, apiKey, roleParams, providerFnSet, providerApiFn;
+		let providerName,
+			modelId,
+			apiKey,
+			roleParams,
+			providerFnSet,
+			providerApiFn,
+			baseUrl,
+			providerResponse,
+			telemetryData = null;
 
 		try {
 			log('info', `New AI service call with role: ${currentRole}`);
@@ -325,6 +389,7 @@ async function _unifiedServiceRunner(serviceType, params) {
 
 			// Pass effectiveProjectRoot to getParametersForRole
 			roleParams = getParametersForRole(currentRole, effectiveProjectRoot);
+			baseUrl = getBaseUrlForRole(currentRole, effectiveProjectRoot);
 
 			// 2. Get Provider Function Set
 			providerFnSet = PROVIDER_FUNCTIONS[providerName?.toLowerCase()];
@@ -401,12 +466,13 @@ async function _unifiedServiceRunner(serviceType, params) {
 				maxTokens: roleParams.maxTokens,
 				temperature: roleParams.temperature,
 				messages,
+				baseUrl,
 				...(serviceType === 'generateObject' && { schema, objectName }),
 				...restApiParams
 			};
 
 			// 6. Attempt the call with retries
-			const result = await _attemptProviderCallWithRetries(
+			providerResponse = await _attemptProviderCallWithRetries(
 				providerApiFn,
 				callParams,
 				providerName,
@@ -414,9 +480,53 @@ async function _unifiedServiceRunner(serviceType, params) {
 				currentRole
 			);
 
-			log('info', `${serviceType}Service succeeded using role: ${currentRole}`);
+			// --- Log Telemetry & Capture Data ---
+			// Use providerResponse which contains the usage data directly for text/object
+			if (userId && providerResponse && providerResponse.usage) {
+				try {
+					telemetryData = await logAiUsage({
+						userId,
+						commandName,
+						providerName,
+						modelId,
+						inputTokens: providerResponse.usage.inputTokens,
+						outputTokens: providerResponse.usage.outputTokens,
+						outputType
+					});
+				} catch (telemetryError) {
+					// logAiUsage already logs its own errors and returns null on failure
+					// No need to log again here, telemetryData will remain null
+				}
+			} else if (userId && providerResponse && !providerResponse.usage) {
+				log(
+					'warn',
+					`Cannot log telemetry for ${commandName} (${providerName}/${modelId}): AI result missing 'usage' data. (May be expected for streams)`
+				);
+			}
+			// --- End Log Telemetry ---
 
-			return result;
+			// --- Extract the correct main result based on serviceType ---
+			let finalMainResult;
+			if (serviceType === 'generateText') {
+				finalMainResult = providerResponse.text;
+			} else if (serviceType === 'generateObject') {
+				finalMainResult = providerResponse.object;
+			} else if (serviceType === 'streamText') {
+				finalMainResult = providerResponse; // Return the whole stream object
+			} else {
+				log(
+					'error',
+					`Unknown serviceType in _unifiedServiceRunner: ${serviceType}`
+				);
+				finalMainResult = providerResponse; // Default to returning the whole object as fallback
+			}
+			// --- End Main Result Extraction ---
+
+			// Return a composite object including the extracted main result and telemetry data
+			return {
+				mainResult: finalMainResult,
+				telemetryData: telemetryData
+			};
 		} catch (error) {
 			const cleanMessage = _extractErrorMessage(error);
 			log(
@@ -461,11 +571,16 @@ async function _unifiedServiceRunner(serviceType, params) {
  * @param {string} [params.projectRoot=null] - Optional project root path for .env fallback.
  * @param {string} params.prompt - The prompt for the AI.
  * @param {string} [params.systemPrompt] - Optional system prompt.
- * // Other specific generateText params can be included here.
+ * @param {string} params.commandName - Name of the command invoking the service.
- * @returns {Promise<string>} The generated text content.
+ * @param {string} [params.outputType='cli'] - 'cli' or 'mcp'.
+ * @returns {Promise<object>} Result object containing generated text and usage data.
  */
 async function generateTextService(params) {
-	return _unifiedServiceRunner('generateText', params);
+	// Ensure default outputType if not provided
+	const defaults = { outputType: 'cli' };
+	const combinedParams = { ...defaults, ...params };
+	// TODO: Validate commandName exists?
+	return _unifiedServiceRunner('generateText', combinedParams);
 }
 
 /**
@@ -478,11 +593,18 @@ async function generateTextService(params) {
  * @param {string} [params.projectRoot=null] - Optional project root path for .env fallback.
  * @param {string} params.prompt - The prompt for the AI.
  * @param {string} [params.systemPrompt] - Optional system prompt.
- * // Other specific streamText params can be included here.
+ * @param {string} params.commandName - Name of the command invoking the service.
- * @returns {Promise<ReadableStream<string>>} A readable stream of text deltas.
+ * @param {string} [params.outputType='cli'] - 'cli' or 'mcp'.
+ * @returns {Promise<object>} Result object containing the stream and usage data.
  */
 async function streamTextService(params) {
-	return _unifiedServiceRunner('streamText', params);
+	const defaults = { outputType: 'cli' };
+	const combinedParams = { ...defaults, ...params };
+	// TODO: Validate commandName exists?
+	// NOTE: Telemetry for streaming might be tricky as usage data often comes at the end.
+	// The current implementation logs *after* the stream is returned.
+	// We might need to adjust how usage is captured/logged for streams.
+	return _unifiedServiceRunner('streamText', combinedParams);
 }
 
 /**
@@ -498,15 +620,89 @@ async function streamTextService(params) {
  * @param {string} [params.systemPrompt] - Optional system prompt.
  * @param {string} [params.objectName='generated_object'] - Name for object/tool.
  * @param {number} [params.maxRetries=3] - Max retries for object generation.
- * @returns {Promise<object>} The generated object matching the schema.
+ * @param {string} params.commandName - Name of the command invoking the service.
+ * @param {string} [params.outputType='cli'] - 'cli' or 'mcp'.
+ * @returns {Promise<object>} Result object containing the generated object and usage data.
  */
 async function generateObjectService(params) {
 	const defaults = {
 		objectName: 'generated_object',
-		maxRetries: 3
+		maxRetries: 3,
+		outputType: 'cli'
 	};
 	const combinedParams = { ...defaults, ...params };
+	// TODO: Validate commandName exists?
 	return _unifiedServiceRunner('generateObject', combinedParams);
 }
 
-export { generateTextService, streamTextService, generateObjectService };
+// --- Telemetry Function ---
+/**
+ * Logs AI usage telemetry data.
+ * For now, it just logs to the console. Sending will be implemented later.
+ * @param {object} params - Telemetry parameters.
+ * @param {string} params.userId - Unique user identifier.
+ * @param {string} params.commandName - The command that triggered the AI call.
+ * @param {string} params.providerName - The AI provider used (e.g., 'openai').
+ * @param {string} params.modelId - The specific AI model ID used.
+ * @param {number} params.inputTokens - Number of input tokens.
+ * @param {number} params.outputTokens - Number of output tokens.
+ */
+async function logAiUsage({
+	userId,
+	commandName,
+	providerName,
+	modelId,
+	inputTokens,
+	outputTokens,
+	outputType
+}) {
+	try {
+		const isMCP = outputType === 'mcp';
+		const timestamp = new Date().toISOString();
+		const totalTokens = (inputTokens || 0) + (outputTokens || 0);
+
+		// Destructure currency along with costs
+		const { inputCost, outputCost, currency } = _getCostForModel(
+			providerName,
+			modelId
+		);
+
+		const totalCost =
+			((inputTokens || 0) / 1_000_000) * inputCost +
+			((outputTokens || 0) / 1_000_000) * outputCost;
+
+		const telemetryData = {
+			timestamp,
+			userId,
+			commandName,
+			modelUsed: modelId, // Consistent field name from requirements
+			providerName, // Keep provider name for context
+			inputTokens: inputTokens || 0,
+			outputTokens: outputTokens || 0,
+			totalTokens,
+			totalCost: parseFloat(totalCost.toFixed(6)),
+			currency // Add currency to the telemetry data
+		};
+
+		if (getDebugFlag()) {
+			log('info', 'AI Usage Telemetry:', telemetryData);
+		}
+
+		// TODO (Subtask 77.2): Send telemetryData securely to the external endpoint.
+
+		return telemetryData;
+	} catch (error) {
+		log('error', `Failed to log AI usage telemetry: ${error.message}`, {
+			error
+		});
+		// Don't re-throw; telemetry failure shouldn't block core functionality.
+		return null;
+	}
+}
+
+export {
+	generateTextService,
+	streamTextService,
+	generateObjectService,
+	logAiUsage
+};

scripts/modules/commands.js

@@ -62,7 +62,8 @@ import {
 	stopLoadingIndicator,
 	displayModelConfiguration,
 	displayAvailableModels,
-	displayApiKeyStatus
+	displayApiKeyStatus,
+	displayAiUsageSummary
 } from './ui.js';
 
 import { initializeProject } from '../init.js';
@@ -73,7 +74,11 @@ import {
 	getApiKeyStatusReport
 } from './task-manager/models.js';
 import { findProjectRoot } from './utils.js';
-
+import {
+	isValidTaskStatus,
+	TASK_STATUS_OPTIONS
+} from '../../src/constants/task-status.js';
+import { getTaskMasterVersion } from '../../src/utils/getVersion.js';
 /**
  * Runs the interactive setup process for model configuration.
  * @param {string|null} projectRoot - The resolved project root directory.
@@ -511,7 +516,7 @@ function registerCommands(programInstance) {
 			const force = options.force || false;
 			const append = options.append || false;
 			let useForce = force;
-			let useAppend = false;
+			let useAppend = append;
 
 			// Helper function to check if tasks.json exists and confirm overwrite
 			async function confirmOverwriteIfNeeded() {
@@ -539,10 +544,10 @@ function registerCommands(programInstance) {
 						if (!(await confirmOverwriteIfNeeded())) return;
 
 						console.log(chalk.blue(`Generating ${numTasks} tasks...`));
-						spinner = ora('Parsing PRD and generating tasks...').start();
+						spinner = ora('Parsing PRD and generating tasks...\n').start();
 						await parsePRD(defaultPrdPath, outputPath, numTasks, {
-							useAppend,
+							append: useAppend, // Changed key from useAppend to append
-							useForce
+							force: useForce // Changed key from useForce to force
 						});
 						spinner.succeed('Tasks generated successfully!');
 						return;
@@ -601,10 +606,10 @@ function registerCommands(programInstance) {
 					console.log(chalk.blue('Appending to existing tasks...'));
 				}
 
-				spinner = ora('Parsing PRD and generating tasks...').start();
+				spinner = ora('Parsing PRD and generating tasks...\n').start();
 				await parsePRD(inputFile, outputPath, numTasks, {
-					append: useAppend,
+					useAppend: useAppend,
-					useForce
+					useForce: useForce
 				});
 				spinner.succeed('Tasks generated successfully!');
 			} catch (error) {
@@ -1033,7 +1038,7 @@ function registerCommands(programInstance) {
 		)
 		.option(
 			'-s, --status <status>',
-			'New status (todo, in-progress, review, done)'
+			`New status (one of: ${TASK_STATUS_OPTIONS.join(', ')})`
 		)
 		.option('-f, --file <file>', 'Path to the tasks file', 'tasks/tasks.json')
 		.action(async (options) => {
@@ -1046,6 +1051,16 @@ function registerCommands(programInstance) {
 				process.exit(1);
 			}
 
+			if (!isValidTaskStatus(status)) {
+				console.error(
+					chalk.red(
+						`Error: Invalid status value: ${status}. Use one of: ${TASK_STATUS_OPTIONS.join(', ')}`
+					)
+				);
+
+				process.exit(1);
+			}
+
 			console.log(
 				chalk.blue(`Setting status of task(s) ${taskId} to: ${status}`)
 			);
@@ -1058,10 +1073,16 @@ function registerCommands(programInstance) {
 		.command('list')
 		.description('List all tasks')
 		.option('-f, --file <file>', 'Path to the tasks file', 'tasks/tasks.json')
+		.option(
+			'-r, --report <report>',
+			'Path to the complexity report file',
+			'scripts/task-complexity-report.json'
+		)
 		.option('-s, --status <status>', 'Filter by status')
 		.option('--with-subtasks', 'Show subtasks for each task')
 		.action(async (options) => {
 			const tasksPath = options.file;
+			const reportPath = options.report;
 			const statusFilter = options.status;
 			const withSubtasks = options.withSubtasks || false;
 
@@ -1073,7 +1094,7 @@ function registerCommands(programInstance) {
 				console.log(chalk.blue('Including subtasks in listing'));
 			}
 
-			await listTasks(tasksPath, statusFilter, withSubtasks);
+			await listTasks(tasksPath, statusFilter, reportPath, withSubtasks);
 		});
 
 	// expand command
@@ -1123,12 +1144,6 @@ function registerCommands(programInstance) {
 						{} // Pass empty context for CLI calls
 						// outputFormat defaults to 'text' in expandAllTasks for CLI
 					);
-					// Optional: Display summary from result
-					console.log(chalk.green(`Expansion Summary:`));
-					console.log(chalk.green(` - Attempted: ${result.tasksToExpand}`));
-					console.log(chalk.green(` - Expanded:  ${result.expandedCount}`));
-					console.log(chalk.yellow(` - Skipped:   ${result.skippedCount}`));
-					console.log(chalk.red(` - Failed:    ${result.failedCount}`));
 				} catch (error) {
 					console.error(
 						chalk.red(`Error expanding all tasks: ${error.message}`)
@@ -1258,7 +1273,7 @@ function registerCommands(programInstance) {
 	// add-task command
 	programInstance
 		.command('add-task')
-		.description('Add a new task using AI or manual input')
+		.description('Add a new task using AI, optionally providing manual details')
 		.option('-f, --file <file>', 'Path to the tasks file', 'tasks/tasks.json')
 		.option(
 			'-p, --prompt <prompt>',
@@ -1273,10 +1288,6 @@ function registerCommands(programInstance) {
 			'--details <details>',
 			'Implementation details (for manual task creation)'
 		)
-		.option(
-			'--test-strategy <testStrategy>',
-			'Test strategy (for manual task creation)'
-		)
 		.option(
 			'--dependencies <dependencies>',
 			'Comma-separated list of task IDs this task depends on'
@@ -1303,74 +1314,70 @@ function registerCommands(programInstance) {
 				process.exit(1);
 			}
 
+			const tasksPath =
+				options.file ||
+				path.join(findProjectRoot() || '.', 'tasks', 'tasks.json') || // Ensure tasksPath is also relative to a found root or current dir
+				'tasks/tasks.json';
+
+			// Correctly determine projectRoot
+			const projectRoot = findProjectRoot();
+
+			let manualTaskData = null;
+			if (isManualCreation) {
+				manualTaskData = {
+					title: options.title,
+					description: options.description,
+					details: options.details || '',
+					testStrategy: options.testStrategy || ''
+				};
+				// Restore specific logging for manual creation
+				console.log(
+					chalk.blue(`Creating task manually with title: "${options.title}"`)
+				);
+			} else {
+				// Restore specific logging for AI creation
+				console.log(
+					chalk.blue(`Creating task with AI using prompt: "${options.prompt}"`)
+				);
+			}
+
+			// Log dependencies and priority if provided (restored)
+			const dependenciesArray = options.dependencies
+				? options.dependencies.split(',').map((id) => id.trim())
+				: [];
+			if (dependenciesArray.length > 0) {
+				console.log(
+					chalk.blue(`Dependencies: [${dependenciesArray.join(', ')}]`)
+				);
+			}
+			if (options.priority) {
+				console.log(chalk.blue(`Priority: ${options.priority}`));
+			}
+
+			const context = {
+				projectRoot,
+				commandName: 'add-task',
+				outputType: 'cli'
+			};
+
 			try {
-				// Prepare dependencies if provided
+				const { newTaskId, telemetryData } = await addTask(
-				let dependencies = [];
+					tasksPath,
-				if (options.dependencies) {
+					options.prompt,
-					dependencies = options.dependencies
+					dependenciesArray,
-						.split(',')
-						.map((id) => parseInt(id.trim(), 10));
-				}
-
-				// Create manual task data if title and description are provided
-				let manualTaskData = null;
-				if (isManualCreation) {
-					manualTaskData = {
-						title: options.title,
-						description: options.description,
-						details: options.details || '',
-						testStrategy: options.testStrategy || ''
-					};
-
-					console.log(
-						chalk.blue(`Creating task manually with title: "${options.title}"`)
-					);
-					if (dependencies.length > 0) {
-						console.log(
-							chalk.blue(`Dependencies: [${dependencies.join(', ')}]`)
-						);
-					}
-					if (options.priority) {
-						console.log(chalk.blue(`Priority: ${options.priority}`));
-					}
-				} else {
-					console.log(
-						chalk.blue(
-							`Creating task with AI using prompt: "${options.prompt}"`
-						)
-					);
-					if (dependencies.length > 0) {
-						console.log(
-							chalk.blue(`Dependencies: [${dependencies.join(', ')}]`)
-						);
-					}
-					if (options.priority) {
-						console.log(chalk.blue(`Priority: ${options.priority}`));
-					}
-				}
-
-				// Pass mcpLog and session for MCP mode
-				const newTaskId = await addTask(
-					options.file,
-					options.prompt, // Pass prompt (will be null/undefined if not provided)
-					dependencies,
 					options.priority,
-					{
+					context,
-						// For CLI, session context isn't directly available like MCP
+					'text',
-						// We don't need to pass session here for CLI API key resolution
+					manualTaskData,
-						// as dotenv loads .env, and utils.resolveEnvVariable checks process.env
+					options.research
-					},
-					'text', // outputFormat
-					manualTaskData, // Pass the potentially created manualTaskData object
-					options.research || false // Pass the research flag value
 				);
 
-				console.log(chalk.green(`✓ Added new task #${newTaskId}`));
+				// addTask handles detailed CLI success logging AND telemetry display when outputFormat is 'text'
-				console.log(chalk.gray('Next: Complete this task or add more tasks'));
+				// No need to call displayAiUsageSummary here anymore.
 			} catch (error) {
 				console.error(chalk.red(`Error adding task: ${error.message}`));
-				if (error.stack && getDebugFlag()) {
+				if (error.details) {
-					console.error(error.stack);
+					console.error(chalk.red(error.details));
 				}
 				process.exit(1);
 			}
@@ -1383,9 +1390,15 @@ function registerCommands(programInstance) {
 			`Show the next task to work on based on dependencies and status${chalk.reset('')}`
 		)
 		.option('-f, --file <file>', 'Path to the tasks file', 'tasks/tasks.json')
+		.option(
+			'-r, --report <report>',
+			'Path to the complexity report file',
+			'scripts/task-complexity-report.json'
+		)
 		.action(async (options) => {
 			const tasksPath = options.file;
-			await displayNextTask(tasksPath);
+			const reportPath = options.report;
+			await displayNextTask(tasksPath, reportPath);
 		});
 
 	// show command
@@ -1398,6 +1411,11 @@ function registerCommands(programInstance) {
 		.option('-i, --id <id>', 'Task ID to show')
 		.option('-s, --status <status>', 'Filter subtasks by status') // ADDED status option
 		.option('-f, --file <file>', 'Path to the tasks file', 'tasks/tasks.json')
+		.option(
+			'-r, --report <report>',
+			'Path to the complexity report file',
+			'scripts/task-complexity-report.json'
+		)
 		.action(async (taskId, options) => {
 			const idArg = taskId || options.id;
 			const statusFilter = options.status; // ADDED: Capture status filter
@@ -1408,8 +1426,9 @@ function registerCommands(programInstance) {
 			}
 
 			const tasksPath = options.file;
+			const reportPath = options.report;
 			// PASS statusFilter to the display function
-			await displayTaskById(tasksPath, idArg, statusFilter);
+			await displayTaskById(tasksPath, idArg, reportPath, statusFilter);
 		});
 
 	// add-dependency command
@@ -1658,6 +1677,7 @@ function registerCommands(programInstance) {
 				}
 			} catch (error) {
 				console.error(chalk.red(`Error: ${error.message}`));
+				showAddSubtaskHelp();
 				process.exit(1);
 			}
 		})
@@ -2064,7 +2084,7 @@ function registerCommands(programInstance) {
 					);
 
 					// Exit with error if any removals failed
-					if (successfulRemovals.length === 0) {
+					if (result.removedTasks.length === 0) {
 						process.exit(1);
 					}
 				}
@@ -2381,28 +2401,7 @@ function setupCLI() {
  */
 async function checkForUpdate() {
 	// Get current version from package.json ONLY
-	let currentVersion = 'unknown'; // Initialize with a default
+	const currentVersion = getTaskMasterVersion();
-	try {
-		// Try to get the version from the installed package (if applicable) or current dir
-		let packageJsonPath = path.join(
-			process.cwd(),
-			'node_modules',
-			'task-master-ai',
-			'package.json'
-		);
-		// Fallback to current directory package.json if not found in node_modules
-		if (!fs.existsSync(packageJsonPath)) {
-			packageJsonPath = path.join(process.cwd(), 'package.json');
-		}
-
-		if (fs.existsSync(packageJsonPath)) {
-			const packageJson = JSON.parse(fs.readFileSync(packageJsonPath, 'utf8'));
-			currentVersion = packageJson.version;
-		}
-	} catch (error) {
-		// Silently fail and use default
-		log('debug', `Error reading current package version: ${error.message}`);
-	}
 
 	return new Promise((resolve) => {
 		// Get the latest version from npm registry

scripts/modules/config-manager.js

@@ -669,6 +669,34 @@ function isConfigFilePresent(explicitRoot = null) {
 	return fs.existsSync(configPath);
 }
 
+/**
+ * Gets the user ID from the configuration.
+ * @param {string|null} explicitRoot - Optional explicit path to the project root.
+ * @returns {string|null} The user ID or null if not found.
+ */
+function getUserId(explicitRoot = null) {
+	const config = getConfig(explicitRoot);
+	if (!config.global) {
+		config.global = {}; // Ensure global object exists
+	}
+	if (!config.global.userId) {
+		config.global.userId = '1234567890';
+		// Attempt to write the updated config.
+		// It's important that writeConfig correctly resolves the path
+		// using explicitRoot, similar to how getConfig does.
+		const success = writeConfig(config, explicitRoot);
+		if (!success) {
+			// Log an error or handle the failure to write,
+			// though for now, we'll proceed with the in-memory default.
+			log(
+				'warning',
+				'Failed to write updated configuration with new userId. Please let the developers know.'
+			);
+		}
+	}
+	return config.global.userId;
+}
+
 /**
  * Gets a list of all provider names defined in the MODEL_MAP.
  * @returns {string[]} An array of provider names.
@@ -677,12 +705,19 @@ function getAllProviders() {
 	return Object.keys(MODEL_MAP || {});
 }
 
+function getBaseUrlForRole(role, explicitRoot = null) {
+	const roleConfig = getModelConfigForRole(role, explicitRoot);
+	return roleConfig && typeof roleConfig.baseUrl === 'string'
+		? roleConfig.baseUrl
+		: undefined;
+}
+
 export {
 	// Core config access
 	getConfig,
 	writeConfig,
-	ConfigurationError, // Export custom error type
+	ConfigurationError,
-	isConfigFilePresent, // Add the new function export
+	isConfigFilePresent,
 
 	// Validation
 	validateProvider,
@@ -704,6 +739,7 @@ export {
 	getFallbackModelId,
 	getFallbackMaxTokens,
 	getFallbackTemperature,
+	getBaseUrlForRole,
 
 	// Global setting getters (No env var overrides)
 	getLogLevel,
@@ -714,7 +750,7 @@ export {
 	getProjectName,
 	getOllamaBaseUrl,
 	getParametersForRole,
-
+	getUserId,
 	// API Key Checkers (still relevant)
 	isApiKeySet,
 	getMcpApiKeyStatus,

scripts/modules/supported-models.json

@@ -99,34 +99,39 @@
 	],
 	"google": [
 		{
-			"id": "gemini-2.5-pro-exp-03-25",
+			"id": "gemini-2.5-pro-preview-05-06",
 			"swe_score": 0.638,
 			"cost_per_1m_tokens": null,
-			"allowed_roles": ["main", "fallback"]
+			"allowed_roles": ["main", "fallback"],
+			"max_tokens": 1048000
+		},
+		{
+			"id": "gemini-2.5-pro-preview-03-25",
+			"swe_score": 0.638,
+			"cost_per_1m_tokens": null,
+			"allowed_roles": ["main", "fallback"],
+			"max_tokens": 1048000
 		},
 		{
 			"id": "gemini-2.5-flash-preview-04-17",
 			"swe_score": 0,
 			"cost_per_1m_tokens": null,
-			"allowed_roles": ["main", "fallback"]
+			"allowed_roles": ["main", "fallback"],
+			"max_tokens": 1048000
 		},
 		{
 			"id": "gemini-2.0-flash",
 			"swe_score": 0.754,
 			"cost_per_1m_tokens": { "input": 0.15, "output": 0.6 },
-			"allowed_roles": ["main", "fallback"]
+			"allowed_roles": ["main", "fallback"],
+			"max_tokens": 1048000
 		},
 		{
-			"id": "gemini-2.0-flash-thinking-experimental",
+			"id": "gemini-2.0-flash-lite",
-			"swe_score": 0.754,
-			"cost_per_1m_tokens": { "input": 0.15, "output": 0.6 },
-			"allowed_roles": ["main", "fallback"]
-		},
-		{
-			"id": "gemini-2.0-pro",
 			"swe_score": 0,
 			"cost_per_1m_tokens": null,
-			"allowed_roles": ["main", "fallback"]
+			"allowed_roles": ["main", "fallback"],
+			"max_tokens": 1048000
 		}
 	],
 	"perplexity": [

scripts/modules/task-manager.js

@@ -23,7 +23,7 @@ import updateSubtaskById from './task-manager/update-subtask-by-id.js';
 import removeTask from './task-manager/remove-task.js';
 import taskExists from './task-manager/task-exists.js';
 import isTaskDependentOn from './task-manager/is-task-dependent.js';
-
+import { readComplexityReport } from './utils.js';
 // Export task manager functions
 export {
 	parsePRD,
@@ -45,5 +45,6 @@ export {
 	removeTask,
 	findTaskById,
 	taskExists,
-	isTaskDependentOn
+	isTaskDependentOn,
+	readComplexityReport
 };

scripts/modules/task-manager/add-task.js

@@ -8,7 +8,8 @@ import {
 	displayBanner,
 	getStatusWithColor,
 	startLoadingIndicator,
-	stopLoadingIndicator
+	stopLoadingIndicator,
+	displayAiUsageSummary
 } from '../ui.js';
 import { readJSON, writeJSON, log as consoleLog, truncate } from '../utils.js';
 import { generateObjectService } from '../ai-services-unified.js';
@@ -44,7 +45,9 @@ const AiTaskDataSchema = z.object({
  * @param {boolean} useResearch - Whether to use the research model (passed to unified service)
  * @param {Object} context - Context object containing session and potentially projectRoot
  * @param {string} [context.projectRoot] - Project root path (for MCP/env fallback)
- * @returns {number} The new task ID
+ * @param {string} [context.commandName] - The name of the command being executed (for telemetry)
+ * @param {string} [context.outputType] - The output type ('cli' or 'mcp', for telemetry)
+ * @returns {Promise<object>} An object containing newTaskId and telemetryData
  */
 async function addTask(
 	tasksPath,
@@ -56,7 +59,7 @@ async function addTask(
 	manualTaskData = null,
 	useResearch = false
 ) {
-	const { session, mcpLog, projectRoot } = context;
+	const { session, mcpLog, projectRoot, commandName, outputType } = context;
 	const isMCP = !!mcpLog;
 
 	// Create a consistent logFn object regardless of context
@@ -78,6 +81,7 @@ async function addTask(
 	);
 
 	let loadingIndicator = null;
+	let aiServiceResponse = null; // To store the full response from AI service
 
 	// Create custom reporter that checks for MCP log
 	const report = (message, level = 'info') => {
@@ -89,20 +93,6 @@ async function addTask(
 	};
 
 	try {
-		// Only display banner and UI elements for text output (CLI)
-		if (outputFormat === 'text') {
-			displayBanner();
-
-			console.log(
-				boxen(chalk.white.bold(`Creating New Task`), {
-					padding: 1,
-					borderColor: 'blue',
-					borderStyle: 'round',
-					margin: { top: 1, bottom: 1 }
-				})
-			);
-		}
-
 		// Read the existing tasks
 		const data = readJSON(tasksPath);
 		if (!data || !data.tasks) {
@@ -169,7 +159,7 @@ async function addTask(
 		} else {
 			report('DEBUG: Taking AI task generation path.', 'debug');
 			// --- Refactored AI Interaction ---
-			report('Generating task data with AI...', 'info');
+			report(`Generating task data with AI with prompt:\n${prompt}`, 'info');
 
 			// Create context string for task creation prompt
 			let contextTasks = '';
@@ -229,29 +219,51 @@ async function addTask(
 			// Start the loading indicator - only for text mode
 			if (outputFormat === 'text') {
 				loadingIndicator = startLoadingIndicator(
-					`Generating new task with ${useResearch ? 'Research' : 'Main'} AI...`
+					`Generating new task with ${useResearch ? 'Research' : 'Main'} AI...\n`
 				);
 			}
 
 			try {
-				// Determine the service role based on the useResearch flag
 				const serviceRole = useResearch ? 'research' : 'main';
-
 				report('DEBUG: Calling generateObjectService...', 'debug');
-				// Call the unified AI service
+
-				const aiGeneratedTaskData = await generateObjectService({
+				aiServiceResponse = await generateObjectService({
-					role: serviceRole, // <-- Use the determined role
+					// Capture the full response
-					session: session, // Pass session for API key resolution
+					role: serviceRole,
-					projectRoot: projectRoot, // <<< Pass projectRoot here
+					session: session,
-					schema: AiTaskDataSchema, // Pass the Zod schema
+					projectRoot: projectRoot,
-					objectName: 'newTaskData', // Name for the object
+					schema: AiTaskDataSchema,
+					objectName: 'newTaskData',
 					systemPrompt: systemPrompt,
-					prompt: userPrompt
+					prompt: userPrompt,
+					commandName: commandName || 'add-task', // Use passed commandName or default
+					outputType: outputType || (isMCP ? 'mcp' : 'cli') // Use passed outputType or derive
 				});
 				report('DEBUG: generateObjectService returned successfully.', 'debug');
 
+				if (!aiServiceResponse || !aiServiceResponse.mainResult) {
+					throw new Error(
+						'AI service did not return the expected object structure.'
+					);
+				}
+
+				// Prefer mainResult if it looks like a valid task object, otherwise try mainResult.object
+				if (
+					aiServiceResponse.mainResult.title &&
+					aiServiceResponse.mainResult.description
+				) {
+					taskData = aiServiceResponse.mainResult;
+				} else if (
+					aiServiceResponse.mainResult.object &&
+					aiServiceResponse.mainResult.object.title &&
+					aiServiceResponse.mainResult.object.description
+				) {
+					taskData = aiServiceResponse.mainResult.object;
+				} else {
+					throw new Error('AI service did not return a valid task object.');
+				}
+
 				report('Successfully generated task data from AI.', 'success');
-				taskData = aiGeneratedTaskData; // Assign the validated object
 			} catch (error) {
 				report(
 					`DEBUG: generateObjectService caught error: ${error.message}`,
@@ -362,11 +374,25 @@ async function addTask(
 					{ padding: 1, borderColor: 'green', borderStyle: 'round' }
 				)
 			);
+
+			// Display AI Usage Summary if telemetryData is available
+			if (
+				aiServiceResponse &&
+				aiServiceResponse.telemetryData &&
+				(outputType === 'cli' || outputType === 'text')
+			) {
+				displayAiUsageSummary(aiServiceResponse.telemetryData, 'cli');
+			}
 		}
 
-		// Return the new task ID
+		report(
-		report(`DEBUG: Returning new task ID: ${newTaskId}`, 'debug');
+			`DEBUG: Returning new task ID: ${newTaskId} and telemetry.`,
-		return newTaskId;
+			'debug'
+		);
+		return {
+			newTaskId: newTaskId,
+			telemetryData: aiServiceResponse ? aiServiceResponse.telemetryData : null
+		};
 	} catch (error) {
 		// Stop any loading indicator on error
 		if (loadingIndicator) {

scripts/modules/task-manager/analyze-task-complexity.js

@@ -4,7 +4,11 @@ import readline from 'readline';
 
 import { log, readJSON, writeJSON, isSilentMode } from '../utils.js';
 
-import { startLoadingIndicator, stopLoadingIndicator } from '../ui.js';
+import {
+	startLoadingIndicator,
+	stopLoadingIndicator,
+	displayAiUsageSummary
+} from '../ui.js';
 
 import { generateTextService } from '../ai-services-unified.js';
 
@@ -196,35 +200,32 @@ async function analyzeTaskComplexity(options, context = {}) {
 		}
 
 		const prompt = generateInternalComplexityAnalysisPrompt(tasksData);
-		// System prompt remains simple for text generation
 		const systemPrompt =
 			'You are an expert software architect and project manager analyzing task complexity. Respond only with the requested valid JSON array.';
 
 		let loadingIndicator = null;
 		if (outputFormat === 'text') {
-			loadingIndicator = startLoadingIndicator('Calling AI service...');
+			loadingIndicator = startLoadingIndicator(
+				`${useResearch ? 'Researching' : 'Analyzing'} the complexity of your tasks with AI...\n`
+			);
 		}
 
-		let fullResponse = ''; // To store the raw text response
+		let aiServiceResponse = null;
+		let complexityAnalysis = null;
 
 		try {
 			const role = useResearch ? 'research' : 'main';
-			reportLog(`Using AI service with role: ${role}`, 'info');
 
-			fullResponse = await generateTextService({
+			aiServiceResponse = await generateTextService({
 				prompt,
 				systemPrompt,
 				role,
 				session,
-				projectRoot
+				projectRoot,
+				commandName: 'analyze-complexity',
+				outputType: mcpLog ? 'mcp' : 'cli'
 			});
 
-			reportLog(
-				'Successfully received text response via AI service',
-				'success'
-			);
-
-			// --- Stop Loading Indicator (Unchanged) ---
 			if (loadingIndicator) {
 				stopLoadingIndicator(loadingIndicator);
 				loadingIndicator = null;
@@ -236,26 +237,18 @@ async function analyzeTaskComplexity(options, context = {}) {
 					chalk.green('AI service call complete. Parsing response...')
 				);
 			}
-			// --- End Stop Loading Indicator ---
 
-			// --- Re-introduce Manual JSON Parsing & Cleanup ---
 			reportLog(`Parsing complexity analysis from text response...`, 'info');
-			let complexityAnalysis;
 			try {
-				let cleanedResponse = fullResponse;
+				let cleanedResponse = aiServiceResponse.mainResult;
-				// Basic trim first
 				cleanedResponse = cleanedResponse.trim();
 
-				// Remove potential markdown code block fences
 				const codeBlockMatch = cleanedResponse.match(
 					/```(?:json)?\s*([\s\S]*?)\s*```/
 				);
 				if (codeBlockMatch) {
-					cleanedResponse = codeBlockMatch[1].trim(); // Trim content inside block
+					cleanedResponse = codeBlockMatch[1].trim();
-					reportLog('Extracted JSON from code block', 'info');
 				} else {
-					// If no code block, ensure it starts with '[' and ends with ']'
-					// This is less robust but a common fallback
 					const firstBracket = cleanedResponse.indexOf('[');
 					const lastBracket = cleanedResponse.lastIndexOf(']');
 					if (firstBracket !== -1 && lastBracket > firstBracket) {
@@ -263,13 +256,11 @@ async function analyzeTaskComplexity(options, context = {}) {
 							firstBracket,
 							lastBracket + 1
 						);
-						reportLog('Extracted content between first [ and last ]', 'info');
 					} else {
 						reportLog(
 							'Warning: Response does not appear to be a JSON array.',
 							'warn'
 						);
-						// Keep going, maybe JSON.parse can handle it or will fail informatively
 					}
 				}
 
@@ -283,48 +274,23 @@ async function analyzeTaskComplexity(options, context = {}) {
 					);
 				}
 
-				try {
+				complexityAnalysis = JSON.parse(cleanedResponse);
-					complexityAnalysis = JSON.parse(cleanedResponse);
+			} catch (parseError) {
-				} catch (jsonError) {
+				if (loadingIndicator) stopLoadingIndicator(loadingIndicator);
-					reportLog(
-						'Initial JSON parsing failed. Raw response might be malformed.',
-						'error'
-					);
-					reportLog(`Original JSON Error: ${jsonError.message}`, 'error');
-					if (outputFormat === 'text' && getDebugFlag(session)) {
-						console.log(chalk.red('--- Start Raw Malformed Response ---'));
-						console.log(chalk.gray(fullResponse));
-						console.log(chalk.red('--- End Raw Malformed Response ---'));
-					}
-					// Re-throw the specific JSON parsing error
-					throw new Error(
-						`Failed to parse JSON response: ${jsonError.message}`
-					);
-				}
-
-				// Ensure it's an array after parsing
-				if (!Array.isArray(complexityAnalysis)) {
-					throw new Error('Parsed response is not a valid JSON array.');
-				}
-			} catch (error) {
-				// Catch errors specifically from the parsing/cleanup block
-				if (loadingIndicator) stopLoadingIndicator(loadingIndicator); // Ensure indicator stops
 				reportLog(
-					`Error parsing complexity analysis JSON: ${error.message}`,
+					`Error parsing complexity analysis JSON: ${parseError.message}`,
 					'error'
 				);
 				if (outputFormat === 'text') {
 					console.error(
 						chalk.red(
-							`Error parsing complexity analysis JSON: ${error.message}`
+							`Error parsing complexity analysis JSON: ${parseError.message}`
 						)
 					);
 				}
-				throw error; // Re-throw parsing error
+				throw parseError;
 			}
-			// --- End Manual JSON Parsing & Cleanup ---
 
-			// --- Post-processing (Missing Task Check) - (Unchanged) ---
 			const taskIds = tasksData.tasks.map((t) => t.id);
 			const analysisTaskIds = complexityAnalysis.map((a) => a.taskId);
 			const missingTaskIds = taskIds.filter(
@@ -359,10 +325,8 @@ async function analyzeTaskComplexity(options, context = {}) {
 					}
 				}
 			}
-			// --- End Post-processing ---
 
-			// --- Report Creation & Writing (Unchanged) ---
+			const report = {
-			const finalReport = {
 				meta: {
 					generatedAt: new Date().toISOString(),
 					tasksAnalyzed: tasksData.tasks.length,
@@ -373,15 +337,13 @@ async function analyzeTaskComplexity(options, context = {}) {
 				complexityAnalysis: complexityAnalysis
 			};
 			reportLog(`Writing complexity report to ${outputPath}...`, 'info');
-			writeJSON(outputPath, finalReport);
+			writeJSON(outputPath, report);
 
 			reportLog(
 				`Task complexity analysis complete. Report written to ${outputPath}`,
 				'success'
 			);
-			// --- End Report Creation & Writing ---
 
-			// --- Display CLI Summary (Unchanged) ---
 			if (outputFormat === 'text') {
 				console.log(
 					chalk.green(
@@ -435,23 +397,28 @@ async function analyzeTaskComplexity(options, context = {}) {
 				if (getDebugFlag(session)) {
 					console.debug(
 						chalk.gray(
-							`Final analysis object: ${JSON.stringify(finalReport, null, 2)}`
+							`Final analysis object: ${JSON.stringify(report, null, 2)}`
 						)
 					);
 				}
-			}
-			// --- End Display CLI Summary ---
 
-			return finalReport;
+				if (aiServiceResponse.telemetryData) {
-		} catch (error) {
+					displayAiUsageSummary(aiServiceResponse.telemetryData, 'cli');
-			// Catches errors from generateTextService call
+				}
+			}
+
+			return {
+				report: report,
+				telemetryData: aiServiceResponse?.telemetryData
+			};
+		} catch (aiError) {
 			if (loadingIndicator) stopLoadingIndicator(loadingIndicator);
-			reportLog(`Error during AI service call: ${error.message}`, 'error');
+			reportLog(`Error during AI service call: ${aiError.message}`, 'error');
 			if (outputFormat === 'text') {
 				console.error(
-					chalk.red(`Error during AI service call: ${error.message}`)
+					chalk.red(`Error during AI service call: ${aiError.message}`)
 				);
-				if (error.message.includes('API key')) {
+				if (aiError.message.includes('API key')) {
 					console.log(
 						chalk.yellow(
 							'\nPlease ensure your API keys are correctly configured in .env or ~/.taskmaster/.env'
@@ -462,10 +429,9 @@ async function analyzeTaskComplexity(options, context = {}) {
 					);
 				}
 			}
-			throw error; // Re-throw AI service error
+			throw aiError;
 		}
 	} catch (error) {
-		// Catches general errors (file read, etc.)
 		reportLog(`Error analyzing task complexity: ${error.message}`, 'error');
 		if (outputFormat === 'text') {
 			console.error(

scripts/modules/task-manager/expand-all-tasks.js

@@ -1,7 +1,14 @@
 import { log, readJSON, isSilentMode } from '../utils.js';
-import { startLoadingIndicator, stopLoadingIndicator } from '../ui.js';
+import {
+	startLoadingIndicator,
+	stopLoadingIndicator,
+	displayAiUsageSummary
+} from '../ui.js';
 import expandTask from './expand-task.js';
 import { getDebugFlag } from '../config-manager.js';
+import { aggregateTelemetry } from '../utils.js';
+import chalk from 'chalk';
+import boxen from 'boxen';
 
 /**
  * Expand all eligible pending or in-progress tasks using the expandTask function.
@@ -14,7 +21,7 @@ import { getDebugFlag } from '../config-manager.js';
  * @param {Object} [context.session] - Session object from MCP.
  * @param {Object} [context.mcpLog] - MCP logger object.
  * @param {string} [outputFormat='text'] - Output format ('text' or 'json'). MCP calls should use 'json'.
- * @returns {Promise<{success: boolean, expandedCount: number, failedCount: number, skippedCount: number, tasksToExpand: number, message?: string}>} - Result summary.
+ * @returns {Promise<{success: boolean, expandedCount: number, failedCount: number, skippedCount: number, tasksToExpand: number, telemetryData: Array<Object>}>} - Result summary.
  */
 async function expandAllTasks(
 	tasksPath,
@@ -51,8 +58,8 @@ async function expandAllTasks(
 	let loadingIndicator = null;
 	let expandedCount = 0;
 	let failedCount = 0;
-	// No skipped count needed now as the filter handles it upfront
+	let tasksToExpandCount = 0;
-	let tasksToExpandCount = 0; // Renamed for clarity
+	const allTelemetryData = []; // Still collect individual data first
 
 	if (!isMCPCall && outputFormat === 'text') {
 		loadingIndicator = startLoadingIndicator(
@@ -90,6 +97,7 @@ async function expandAllTasks(
 				failedCount: 0,
 				skippedCount: 0,
 				tasksToExpand: 0,
+				telemetryData: allTelemetryData,
 				message: 'No tasks eligible for expansion.'
 			};
 			// --- End Fix ---
@@ -97,19 +105,6 @@ async function expandAllTasks(
 
 		// Iterate over the already filtered tasks
 		for (const task of tasksToExpand) {
-			// --- Remove Redundant Check ---
-			// The check below is no longer needed as the initial filter handles it
-			/*
-			if (task.subtasks && task.subtasks.length > 0 && !force) {
-				logger.info(
-					`Skipping task ${task.id}: Already has subtasks. Use --force to overwrite.`
-				);
-				skippedCount++;
-				continue;
-			}
-			*/
-			// --- End Removed Redundant Check ---
-
 			// Start indicator for individual task expansion in CLI mode
 			let taskIndicator = null;
 			if (!isMCPCall && outputFormat === 'text') {
@@ -117,17 +112,23 @@ async function expandAllTasks(
 			}
 
 			try {
-				// Call the refactored expandTask function
+				// Call the refactored expandTask function AND capture result
-				await expandTask(
+				const result = await expandTask(
 					tasksPath,
 					task.id,
-					numSubtasks, // Pass numSubtasks, expandTask handles defaults/complexity
+					numSubtasks,
 					useResearch,
 					additionalContext,
 					context, // Pass the whole context object { session, mcpLog }
-					force // Pass the force flag down
+					force
 				);
 				expandedCount++;
+
+				// Collect individual telemetry data
+				if (result && result.telemetryData) {
+					allTelemetryData.push(result.telemetryData);
+				}
+
 				if (taskIndicator) {
 					stopLoadingIndicator(taskIndicator, `Task ${task.id} expanded.`);
 				}
@@ -146,18 +147,48 @@ async function expandAllTasks(
 			}
 		}
 
-		// Log final summary (removed skipped count from message)
+		// --- AGGREGATION AND DISPLAY ---
 		logger.info(
 			`Expansion complete: ${expandedCount} expanded, ${failedCount} failed.`
 		);
 
-		// Return summary (skippedCount is now 0) - Add success: true here as well for consistency
+		// Aggregate the collected telemetry data
+		const aggregatedTelemetryData = aggregateTelemetry(
+			allTelemetryData,
+			'expand-all-tasks'
+		);
+
+		if (outputFormat === 'text') {
+			const summaryContent =
+				`${chalk.white.bold('Expansion Summary:')}\n\n` +
+				`${chalk.cyan('-')} Attempted: ${chalk.bold(tasksToExpandCount)}\n` +
+				`${chalk.green('-')} Expanded:  ${chalk.bold(expandedCount)}\n` +
+				// Skipped count is always 0 now due to pre-filtering
+				`${chalk.gray('-')} Skipped:   ${chalk.bold(0)}\n` +
+				`${chalk.red('-')} Failed:    ${chalk.bold(failedCount)}`;
+
+			console.log(
+				boxen(summaryContent, {
+					padding: 1,
+					margin: { top: 1 },
+					borderColor: failedCount > 0 ? 'red' : 'green', // Red if failures, green otherwise
+					borderStyle: 'round'
+				})
+			);
+		}
+
+		if (outputFormat === 'text' && aggregatedTelemetryData) {
+			displayAiUsageSummary(aggregatedTelemetryData, 'cli');
+		}
+
+		// Return summary including the AGGREGATED telemetry data
 		return {
-			success: true, // Indicate overall success
+			success: true,
 			expandedCount,
 			failedCount,
 			skippedCount: 0,
-			tasksToExpand: tasksToExpandCount
+			tasksToExpand: tasksToExpandCount,
+			telemetryData: aggregatedTelemetryData
 		};
 	} catch (error) {
 		if (loadingIndicator)

scripts/modules/task-manager/expand-task.js

@@ -4,7 +4,11 @@ import { z } from 'zod';
 
 import { log, readJSON, writeJSON, isSilentMode } from '../utils.js';
 
-import { startLoadingIndicator, stopLoadingIndicator } from '../ui.js';
+import {
+	startLoadingIndicator,
+	stopLoadingIndicator,
+	displayAiUsageSummary
+} from '../ui.js';
 
 import { generateTextService } from '../ai-services-unified.js';
 
@@ -142,7 +146,7 @@ function generateResearchUserPrompt(
       "id": <number>, // Sequential ID starting from ${nextSubtaskId}
       "title": "<string>",
       "description": "<string>",
-      "dependencies": [<number>], // e.g., [${nextSubtaskId + 1}]
+      "dependencies": [<number>], // e.g., [${nextSubtaskId + 1}]. If no dependencies, use an empty array [].
       "details": "<string>",
       "testStrategy": "<string>" // Optional
     },
@@ -162,6 +166,8 @@ ${contextPrompt}
 CRITICAL: Respond ONLY with a valid JSON object containing a single key "subtasks". The value must be an array of the generated subtasks, strictly matching this structure:
 ${schemaDescription}
 
+Important: For the 'dependencies' field, if a subtask has no dependencies, you MUST use an empty array, for example: "dependencies": []. Do not use null or omit the field.
+
 Do not include ANY explanatory text, markdown, or code block markers. Just the JSON object.`;
 }
 
@@ -182,77 +188,153 @@ function parseSubtasksFromText(
 	parentTaskId,
 	logger
 ) {
-	logger.info('Attempting to parse subtasks object from text response...');
+	if (typeof text !== 'string') {
-	if (!text || text.trim() === '') {
+		logger.error(
-		throw new Error('AI response text is empty.');
+			`AI response text is not a string. Received type: ${typeof text}, Value: ${text}`
+		);
+		throw new Error('AI response text is not a string.');
 	}
 
-	let cleanedResponse = text.trim();
+	if (!text || text.trim() === '') {
-	const originalResponseForDebug = cleanedResponse;
+		throw new Error('AI response text is empty after trimming.');
+	}
 
-	// 1. Extract from Markdown code block first
+	const originalTrimmedResponse = text.trim(); // Store the original trimmed response
-	const codeBlockMatch = cleanedResponse.match(
+	let jsonToParse = originalTrimmedResponse; // Initialize jsonToParse with it
-		/```(?:json)?\s*([\s\S]*?)\s*```/
+
+	logger.debug(
+		`Original AI Response for parsing (full length: ${jsonToParse.length}): ${jsonToParse.substring(0, 1000)}...`
 	);
-	if (codeBlockMatch) {
+
-		cleanedResponse = codeBlockMatch[1].trim();
+	// --- Pre-emptive cleanup for known AI JSON issues ---
-		logger.info('Extracted JSON content from Markdown code block.');
+	// Fix for "dependencies": , or "dependencies":,
-	} else {
+	if (jsonToParse.includes('"dependencies":')) {
-		// 2. If no code block, find first '{' and last '}' for the object
+		const malformedPattern = /"dependencies":\s*,/g;
-		const firstBrace = cleanedResponse.indexOf('{');
+		if (malformedPattern.test(jsonToParse)) {
-		const lastBrace = cleanedResponse.lastIndexOf('}');
+			logger.warn('Attempting to fix malformed "dependencies": , issue.');
-		if (firstBrace !== -1 && lastBrace > firstBrace) {
+			jsonToParse = jsonToParse.replace(
-			cleanedResponse = cleanedResponse.substring(firstBrace, lastBrace + 1);
+				malformedPattern,
-			logger.info('Extracted content between first { and last }.');
+				'"dependencies": [],'
+			);
+			logger.debug(
+				`JSON after fixing "dependencies": ${jsonToParse.substring(0, 500)}...`
+			);
+		}
+	}
+	// --- End pre-emptive cleanup ---
+
+	let parsedObject;
+	let primaryParseAttemptFailed = false;
+
+	// --- Attempt 1: Simple Parse (with optional Markdown cleanup) ---
+	logger.debug('Attempting simple parse...');
+	try {
+		// Check for markdown code block
+		const codeBlockMatch = jsonToParse.match(/```(?:json)?\s*([\s\S]*?)\s*```/);
+		let contentToParseDirectly = jsonToParse;
+		if (codeBlockMatch && codeBlockMatch[1]) {
+			contentToParseDirectly = codeBlockMatch[1].trim();
+			logger.debug('Simple parse: Extracted content from markdown code block.');
 		} else {
+			logger.debug(
+				'Simple parse: No markdown code block found, using trimmed original.'
+			);
+		}
+
+		parsedObject = JSON.parse(contentToParseDirectly);
+		logger.debug('Simple parse successful!');
+
+		// Quick check if it looks like our target object
+		if (
+			!parsedObject ||
+			typeof parsedObject !== 'object' ||
+			!Array.isArray(parsedObject.subtasks)
+		) {
 			logger.warn(
-				'Response does not appear to contain a JSON object structure. Parsing raw response.'
+				'Simple parse succeeded, but result is not the expected {"subtasks": []} structure. Will proceed to advanced extraction.'
+			);
+			primaryParseAttemptFailed = true;
+			parsedObject = null; // Reset parsedObject so we enter the advanced logic
+		}
+		// If it IS the correct structure, we'll skip advanced extraction.
+	} catch (e) {
+		logger.warn(
+			`Simple parse failed: ${e.message}. Proceeding to advanced extraction logic.`
+		);
+		primaryParseAttemptFailed = true;
+		// jsonToParse is already originalTrimmedResponse if simple parse failed before modifying it for markdown
+	}
+
+	// --- Attempt 2: Advanced Extraction (if simple parse failed or produced wrong structure) ---
+	if (primaryParseAttemptFailed || !parsedObject) {
+		// Ensure we try advanced if simple parse gave wrong structure
+		logger.debug('Attempting advanced extraction logic...');
+		// Reset jsonToParse to the original full trimmed response for advanced logic
+		jsonToParse = originalTrimmedResponse;
+
+		// (Insert the more complex extraction logic here - the one we worked on with:
+		//  - targetPattern = '{"subtasks":';
+		//  - careful brace counting for that targetPattern
+		//  - fallbacks to last '{' and '}' if targetPattern logic fails)
+		//  This was the logic from my previous message. Let's assume it's here.
+		//  This block should ultimately set `jsonToParse` to the best candidate string.
+
+		// Example snippet of that advanced logic's start:
+		const targetPattern = '{"subtasks":';
+		const patternStartIndex = jsonToParse.indexOf(targetPattern);
+
+		if (patternStartIndex !== -1) {
+			let openBraces = 0;
+			let firstBraceFound = false;
+			let extractedJsonBlock = '';
+			// ... (loop for brace counting as before) ...
+			// ... (if successful, jsonToParse = extractedJsonBlock) ...
+			// ... (if that fails, fallbacks as before) ...
+		} else {
+			// ... (fallback to last '{' and '}' if targetPattern not found) ...
+		}
+		// End of advanced logic excerpt
+
+		logger.debug(
+			`Advanced extraction: JSON string that will be parsed: ${jsonToParse.substring(0, 500)}...`
+		);
+		try {
+			parsedObject = JSON.parse(jsonToParse);
+			logger.debug('Advanced extraction parse successful!');
+		} catch (parseError) {
+			logger.error(
+				`Advanced extraction: Failed to parse JSON object: ${parseError.message}`
+			);
+			logger.error(
+				`Advanced extraction: Problematic JSON string for parse (first 500 chars): ${jsonToParse.substring(0, 500)}`
+			);
+			throw new Error( // Re-throw a more specific error if advanced also fails
+				`Failed to parse JSON response object after both simple and advanced attempts: ${parseError.message}`
 			);
 		}
 	}
 
-	// 3. Attempt to parse the object
+	// --- Validation (applies to successfully parsedObject from either attempt) ---
-	let parsedObject;
-	try {
-		parsedObject = JSON.parse(cleanedResponse);
-	} catch (parseError) {
-		logger.error(`Failed to parse JSON object: ${parseError.message}`);
-		logger.error(
-			`Problematic JSON string (first 500 chars): ${cleanedResponse.substring(0, 500)}`
-		);
-		logger.error(
-			`Original Raw Response (first 500 chars): ${originalResponseForDebug.substring(0, 500)}`
-		);
-		throw new Error(
-			`Failed to parse JSON response object: ${parseError.message}`
-		);
-	}
-
-	// 4. Validate the object structure and extract the subtasks array
 	if (
 		!parsedObject ||
 		typeof parsedObject !== 'object' ||
 		!Array.isArray(parsedObject.subtasks)
 	) {
 		logger.error(
-			`Parsed content is not an object or missing 'subtasks' array. Content: ${JSON.stringify(parsedObject).substring(0, 200)}`
+			`Final parsed content is not an object or missing 'subtasks' array. Content: ${JSON.stringify(parsedObject).substring(0, 200)}`
 		);
 		throw new Error(
-			'Parsed AI response is not a valid object containing a "subtasks" array.'
+			'Parsed AI response is not a valid object containing a "subtasks" array after all attempts.'
 		);
 	}
-	const parsedSubtasks = parsedObject.subtasks; // Extract the array
+	const parsedSubtasks = parsedObject.subtasks;
 
-	logger.info(
-		`Successfully parsed ${parsedSubtasks.length} potential subtasks from the object.`
-	);
 	if (expectedCount && parsedSubtasks.length !== expectedCount) {
 		logger.warn(
 			`Expected ${expectedCount} subtasks, but parsed ${parsedSubtasks.length}.`
 		);
 	}
 
-	// 5. Validate and Normalize each subtask using Zod schema
 	let currentId = startId;
 	const validatedSubtasks = [];
 	const validationErrors = [];
@@ -260,22 +342,21 @@ function parseSubtasksFromText(
 	for (const rawSubtask of parsedSubtasks) {
 		const correctedSubtask = {
 			...rawSubtask,
-			id: currentId, // Enforce sequential ID
+			id: currentId,
 			dependencies: Array.isArray(rawSubtask.dependencies)
 				? rawSubtask.dependencies
 						.map((dep) => (typeof dep === 'string' ? parseInt(dep, 10) : dep))
 						.filter(
 							(depId) => !isNaN(depId) && depId >= startId && depId < currentId
-						) // Ensure deps are numbers, valid range
+						)
 				: [],
-			status: 'pending' // Enforce pending status
+			status: 'pending'
-			// parentTaskId can be added if needed: parentTaskId: parentTaskId
 		};
 
 		const result = subtaskSchema.safeParse(correctedSubtask);
 
 		if (result.success) {
-			validatedSubtasks.push(result.data); // Add the validated data
+			validatedSubtasks.push(result.data);
 		} else {
 			logger.warn(
 				`Subtask validation failed for raw data: ${JSON.stringify(rawSubtask).substring(0, 100)}...`
@@ -285,18 +366,14 @@ function parseSubtasksFromText(
 				logger.warn(errorMessage);
 				validationErrors.push(`Subtask ${currentId}: ${errorMessage}`);
 			});
-			// Optionally, decide whether to include partially valid tasks or skip them
-			// For now, we'll skip invalid ones
 		}
-		currentId++; // Increment ID for the next *potential* subtask
+		currentId++;
 	}
 
 	if (validationErrors.length > 0) {
 		logger.error(
 			`Found ${validationErrors.length} validation errors in the generated subtasks.`
 		);
-		// Optionally throw an error here if strict validation is required
-		// throw new Error(`Subtask validation failed:\n${validationErrors.join('\n')}`);
 		logger.warn('Proceeding with only the successfully validated subtasks.');
 	}
 
@@ -305,8 +382,6 @@ function parseSubtasksFromText(
 			'AI response contained potential subtasks, but none passed validation.'
 		);
 	}
-
-	// Ensure we don't return more than expected, preferring validated ones
 	return validatedSubtasks.slice(0, expectedCount || validatedSubtasks.length);
 }
 
@@ -336,9 +411,13 @@ async function expandTask(
 	context = {},
 	force = false
 ) {
-	const { session, mcpLog } = context;
+	const { session, mcpLog, projectRoot: contextProjectRoot } = context;
 	const outputFormat = mcpLog ? 'json' : 'text';
 
+	// Determine projectRoot: Use from context if available, otherwise derive from tasksPath
+	const projectRoot =
+		contextProjectRoot || path.dirname(path.dirname(tasksPath));
+
 	// Use mcpLog if available, otherwise use the default console log wrapper
 	const logger = mcpLog || {
 		info: (msg) => !isSilentMode() && log('info', msg),
@@ -363,7 +442,9 @@ async function expandTask(
 		);
 		if (taskIndex === -1) throw new Error(`Task ${taskId} not found`);
 		const task = data.tasks[taskIndex];
-		logger.info(`Expanding task ${taskId}: ${task.title}`);
+		logger.info(
+			`Expanding task ${taskId}: ${task.title}${useResearch ? ' with research' : ''}`
+		);
 		// --- End Task Loading/Filtering ---
 
 		// --- Handle Force Flag: Clear existing subtasks if force=true ---
@@ -381,7 +462,6 @@ async function expandTask(
 		let complexityReasoningContext = '';
 		let systemPrompt; // Declare systemPrompt here
 
-		const projectRoot = path.dirname(path.dirname(tasksPath));
 		const complexityReportPath = path.join(
 			projectRoot,
 			'scripts/task-complexity-report.json'
@@ -488,28 +568,27 @@ async function expandTask(
 		let loadingIndicator = null;
 		if (outputFormat === 'text') {
 			loadingIndicator = startLoadingIndicator(
-				`Generating ${finalSubtaskCount} subtasks...`
+				`Generating ${finalSubtaskCount} subtasks...\n`
 			);
 		}
 
 		let responseText = '';
+		let aiServiceResponse = null;
 
 		try {
 			const role = useResearch ? 'research' : 'main';
-			logger.info(`Using AI service with role: ${role}`);
 
-			// Call generateTextService with the determined prompts
+			// Call generateTextService with the determined prompts and telemetry params
-			responseText = await generateTextService({
+			aiServiceResponse = await generateTextService({
 				prompt: promptContent,
-				systemPrompt: systemPrompt, // Use the determined system prompt
+				systemPrompt: systemPrompt,
 				role,
 				session,
-				projectRoot
+				projectRoot,
+				commandName: 'expand-task',
+				outputType: outputFormat
 			});
-			logger.info(
+			responseText = aiServiceResponse.mainResult;
-				'Successfully received text response from AI service',
-				'success'
-			);
 
 			// Parse Subtasks
 			generatedSubtasks = parseSubtasksFromText(
@@ -550,14 +629,23 @@ async function expandTask(
 		// --- End Change: Append instead of replace ---
 
 		data.tasks[taskIndex] = task; // Assign the modified task back
-		logger.info(`Writing updated tasks to ${tasksPath}`);
 		writeJSON(tasksPath, data);
-		logger.info(`Generating individual task files...`);
 		await generateTaskFiles(tasksPath, path.dirname(tasksPath));
-		logger.info(`Task files generated.`);
-		// --- End Task Update & File Writing ---
 
-		return task; // Return the updated task object
+		// Display AI Usage Summary for CLI
+		if (
+			outputFormat === 'text' &&
+			aiServiceResponse &&
+			aiServiceResponse.telemetryData
+		) {
+			displayAiUsageSummary(aiServiceResponse.telemetryData, 'cli');
+		}
+
+		// Return the updated task object AND telemetry data
+		return {
+			task,
+			telemetryData: aiServiceResponse?.telemetryData
+		};
 	} catch (error) {
 		// Catches errors from file reading, parsing, AI call etc.
 		logger.error(`Error expanding task ${taskId}: ${error.message}`, 'error');

scripts/modules/task-manager/find-next-task.js

@@ -1,3 +1,6 @@
+import { log } from '../utils.js';
+import { addComplexityToTask } from '../utils.js';
+
 /**
  * Return the next work item:
  *   •  Prefer an eligible SUBTASK that belongs to any parent task
@@ -15,9 +18,10 @@
  *  ─ parentId      →  number  (present only when it's a subtask)
  *
  * @param {Object[]} tasks  – full array of top-level tasks, each may contain .subtasks[]
+ * @param {Object} [complexityReport=null] - Optional complexity report object
  * @returns {Object|null}   – next work item or null if nothing is eligible
  */
-function findNextTask(tasks) {
+function findNextTask(tasks, complexityReport = null) {
 	// ---------- helpers ----------------------------------------------------
 	const priorityValues = { high: 3, medium: 2, low: 1 };
 
@@ -91,7 +95,14 @@ function findNextTask(tasks) {
 			if (aPar !== bPar) return aPar - bPar;
 			return aSub - bSub;
 		});
-		return candidateSubtasks[0];
+		const nextTask = candidateSubtasks[0];
+
+		// Add complexity to the task before returning
+		if (nextTask && complexityReport) {
+			addComplexityToTask(nextTask, complexityReport);
+		}
+
+		return nextTask;
 	}
 
 	// ---------- 2) fall back to top-level tasks (original logic) ------------
@@ -116,6 +127,11 @@ function findNextTask(tasks) {
 		return a.id - b.id;
 	})[0];
 
+	// Add complexity to the task before returning
+	if (nextTask && complexityReport) {
+		addComplexityToTask(nextTask, complexityReport);
+	}
+
 	return nextTask;
 }
 

scripts/modules/task-manager/generate-task-files.js

@@ -19,8 +19,6 @@ function generateTaskFiles(tasksPath, outputDir, options = {}) {
 		// Determine if we're in MCP mode by checking for mcpLog
 		const isMcpMode = !!options?.mcpLog;
 
-		log('info', `Preparing to regenerate task files in ${tasksPath}`);
-
 		const data = readJSON(tasksPath);
 		if (!data || !data.tasks) {
 			throw new Error(`No valid tasks found in ${tasksPath}`);
@@ -31,7 +29,7 @@ function generateTaskFiles(tasksPath, outputDir, options = {}) {
 			fs.mkdirSync(outputDir, { recursive: true });
 		}
 
-		log('info', `Found ${data.tasks.length} tasks to regenerate`);
+		log('info', `Preparing to regenerate ${data.tasks.length} task files`);
 
 		// Validate and fix dependencies before generating files
 		log('info', `Validating and fixing dependencies`);

scripts/modules/task-manager/list-tasks.js

@@ -2,13 +2,20 @@ import chalk from 'chalk';
 import boxen from 'boxen';
 import Table from 'cli-table3';
 
-import { log, readJSON, truncate } from '../utils.js';
+import {
+	log,
+	readJSON,
+	truncate,
+	readComplexityReport,
+	addComplexityToTask
+} from '../utils.js';
 import findNextTask from './find-next-task.js';
 
 import {
 	displayBanner,
 	getStatusWithColor,
 	formatDependenciesWithStatus,
+	getComplexityWithColor,
 	createProgressBar
 } from '../ui.js';
 
@@ -16,6 +23,7 @@ import {
  * List all tasks
  * @param {string} tasksPath - Path to the tasks.json file
  * @param {string} statusFilter - Filter by status
+ * @param {string} reportPath - Path to the complexity report
  * @param {boolean} withSubtasks - Whether to show subtasks
  * @param {string} outputFormat - Output format (text or json)
  * @returns {Object} - Task list result for json format
@@ -23,6 +31,7 @@ import {
 function listTasks(
 	tasksPath,
 	statusFilter,
+	reportPath = null,
 	withSubtasks = false,
 	outputFormat = 'text'
 ) {
@@ -37,6 +46,13 @@ function listTasks(
 			throw new Error(`No valid tasks found in ${tasksPath}`);
 		}
 
+		// Add complexity scores to tasks if report exists
+		const complexityReport = readComplexityReport(reportPath);
+		// Apply complexity scores to tasks
+		if (complexityReport && complexityReport.complexityAnalysis) {
+			data.tasks.forEach((task) => addComplexityToTask(task, complexityReport));
+		}
+
 		// Filter tasks by status if specified
 		const filteredTasks =
 			statusFilter && statusFilter.toLowerCase() !== 'all' // <-- Added check for 'all'
@@ -257,8 +273,8 @@ function listTasks(
 		);
 		const avgDependenciesPerTask = totalDependencies / data.tasks.length;
 
-		// Find next task to work on
+		// Find next task to work on, passing the complexity report
-		const nextItem = findNextTask(data.tasks);
+		const nextItem = findNextTask(data.tasks, complexityReport);
 
 		// Get terminal width - more reliable method
 		let terminalWidth;
@@ -301,8 +317,11 @@ function listTasks(
 			`${chalk.blue('•')} ${chalk.white('Avg dependencies per task:')} ${avgDependenciesPerTask.toFixed(1)}\n\n` +
 			chalk.cyan.bold('Next Task to Work On:') +
 			'\n' +
-			`ID: ${chalk.cyan(nextItem ? nextItem.id : 'N/A')} - ${nextItem ? chalk.white.bold(truncate(nextItem.title, 40)) : chalk.yellow('No task available')}\n` +
+			`ID: ${chalk.cyan(nextItem ? nextItem.id : 'N/A')} - ${nextItem ? chalk.white.bold(truncate(nextItem.title, 40)) : chalk.yellow('No task available')}
-			`Priority: ${nextItem ? chalk.white(nextItem.priority || 'medium') : ''}  Dependencies: ${nextItem ? formatDependenciesWithStatus(nextItem.dependencies, data.tasks, true) : ''}`;
+` +
+			`Priority: ${nextItem ? chalk.white(nextItem.priority || 'medium') : ''}  Dependencies: ${nextItem ? formatDependenciesWithStatus(nextItem.dependencies, data.tasks, true, complexityReport) : ''}
+` +
+			`Complexity: ${nextItem && nextItem.complexityScore ? getComplexityWithColor(nextItem.complexityScore) : chalk.gray('N/A')}`;
 
 		// Calculate width for side-by-side display
 		// Box borders, padding take approximately 4 chars on each side
@@ -412,9 +431,16 @@ function listTasks(
 		// Make dependencies column smaller as requested (-20%)
 		const depsWidthPct = 20;
 
+		const complexityWidthPct = 10;
+
 		// Calculate title/description width as remaining space (+20% from dependencies reduction)
 		const titleWidthPct =
-			100 - idWidthPct - statusWidthPct - priorityWidthPct - depsWidthPct;
+			100 -
+			idWidthPct -
+			statusWidthPct -
+			priorityWidthPct -
+			depsWidthPct -
+			complexityWidthPct;
 
 		// Allow 10 characters for borders and padding
 		const availableWidth = terminalWidth - 10;
@@ -424,6 +450,9 @@ function listTasks(
 		const statusWidth = Math.floor(availableWidth * (statusWidthPct / 100));
 		const priorityWidth = Math.floor(availableWidth * (priorityWidthPct / 100));
 		const depsWidth = Math.floor(availableWidth * (depsWidthPct / 100));
+		const complexityWidth = Math.floor(
+			availableWidth * (complexityWidthPct / 100)
+		);
 		const titleWidth = Math.floor(availableWidth * (titleWidthPct / 100));
 
 		// Create a table with correct borders and spacing
@@ -433,9 +462,17 @@ function listTasks(
 				chalk.cyan.bold('Title'),
 				chalk.cyan.bold('Status'),
 				chalk.cyan.bold('Priority'),
-				chalk.cyan.bold('Dependencies')
+				chalk.cyan.bold('Dependencies'),
+				chalk.cyan.bold('Complexity')
+			],
+			colWidths: [
+				idWidth,
+				titleWidth,
+				statusWidth,
+				priorityWidth,
+				depsWidth,
+				complexityWidth // Added complexity column width
 			],
-			colWidths: [idWidth, titleWidth, statusWidth, priorityWidth, depsWidth],
 			style: {
 				head: [], // No special styling for header
 				border: [], // No special styling for border
@@ -454,7 +491,8 @@ function listTasks(
 				depText = formatDependenciesWithStatus(
 					task.dependencies,
 					data.tasks,
-					true
+					true,
+					complexityReport
 				);
 			} else {
 				depText = chalk.gray('None');
@@ -480,7 +518,10 @@ function listTasks(
 				truncate(cleanTitle, titleWidth - 3),
 				status,
 				priorityColor(truncate(task.priority || 'medium', priorityWidth - 2)),
-				depText // No truncation for dependencies
+				depText,
+				task.complexityScore
+					? getComplexityWithColor(task.complexityScore)
+					: chalk.gray('N/A')
 			]);
 
 			// Add subtasks if requested
@@ -516,6 +557,8 @@ function listTasks(
 								// Default to regular task dependency
 								const depTask = data.tasks.find((t) => t.id === depId);
 								if (depTask) {
+									// Add complexity to depTask before checking status
+									addComplexityToTask(depTask, complexityReport);
 									const isDone =
 										depTask.status === 'done' || depTask.status === 'completed';
 									const isInProgress = depTask.status === 'in-progress';
@@ -541,7 +584,10 @@ function listTasks(
 						chalk.dim(`└─ ${truncate(subtask.title, titleWidth - 5)}`),
 						getStatusWithColor(subtask.status, true),
 						chalk.dim('-'),
-						subtaskDepText // No truncation for dependencies
+						subtaskDepText,
+						subtask.complexityScore
+							? chalk.gray(`${subtask.complexityScore}`)
+							: chalk.gray('N/A')
 					]);
 				});
 			}
@@ -597,6 +643,8 @@ function listTasks(
 				subtasksSection = `\n\n${chalk.white.bold('Subtasks:')}\n`;
 				subtasksSection += parentTaskForSubtasks.subtasks
 					.map((subtask) => {
+						// Add complexity to subtask before display
+						addComplexityToTask(subtask, complexityReport);
 						// Using a more simplified format for subtask status display
 						const status = subtask.status || 'pending';
 						const statusColors = {
@@ -625,8 +673,8 @@ function listTasks(
 						'\n\n' +
 						// Use nextItem.priority, nextItem.status, nextItem.dependencies
 						`${chalk.white('Priority:')} ${priorityColors[nextItem.priority || 'medium'](nextItem.priority || 'medium')}   ${chalk.white('Status:')} ${getStatusWithColor(nextItem.status, true)}\n` +
-						`${chalk.white('Dependencies:')} ${nextItem.dependencies && nextItem.dependencies.length > 0 ? formatDependenciesWithStatus(nextItem.dependencies, data.tasks, true) : chalk.gray('None')}\n\n` +
+						`${chalk.white('Dependencies:')} ${nextItem.dependencies && nextItem.dependencies.length > 0 ? formatDependenciesWithStatus(nextItem.dependencies, data.tasks, true, complexityReport) : chalk.gray('None')}\n\n` +
-						// Use nextItem.description (Note: findNextTask doesn't return description, need to fetch original task/subtask for this)
+						// Use nextTask.description (Note: findNextTask doesn't return description, need to fetch original task/subtask for this)
 						// *** Fetching original item for description and details ***
 						`${chalk.white('Description:')} ${getWorkItemDescription(nextItem, data.tasks)}` +
 						subtasksSection + // <-- Subtasks are handled above now

scripts/modules/task-manager/parse-prd.js

@@ -17,6 +17,7 @@ import {
 import { generateObjectService } from '../ai-services-unified.js';
 import { getDebugFlag } from '../config-manager.js';
 import generateTaskFiles from './generate-task-files.js';
+import { displayAiUsageSummary } from '../ui.js';
 
 // Define the Zod schema for a SINGLE task object
 const prdSingleTaskSchema = z.object({
@@ -47,8 +48,8 @@ const prdResponseSchema = z.object({
  * @param {string} tasksPath - Path to the tasks.json file
  * @param {number} numTasks - Number of tasks to generate
  * @param {Object} options - Additional options
- * @param {boolean} [options.useForce=false] - Whether to overwrite existing tasks.json.
+ * @param {boolean} [options.force=false] - Whether to overwrite existing tasks.json.
- * @param {boolean} [options.useAppend=false] - Append to existing tasks file.
+ * @param {boolean} [options.append=false] - Append to existing tasks file.
  * @param {Object} [options.reportProgress] - Function to report progress (optional, likely unused).
  * @param {Object} [options.mcpLog] - MCP logger object (optional).
  * @param {Object} [options.session] - Session object from MCP server (optional).
@@ -61,8 +62,8 @@ async function parsePRD(prdPath, tasksPath, numTasks, options = {}) {
 		mcpLog,
 		session,
 		projectRoot,
-		useForce = false,
+		force = false,
-		useAppend = false
+		append = false
 	} = options;
 	const isMCP = !!mcpLog;
 	const outputFormat = isMCP ? 'json' : 'text';
@@ -89,17 +90,16 @@ async function parsePRD(prdPath, tasksPath, numTasks, options = {}) {
 		}
 	};
 
-	report(
+	report(`Parsing PRD file: ${prdPath}, Force: ${force}, Append: ${append}`);
-		`Parsing PRD file: ${prdPath}, Force: ${useForce}, Append: ${useAppend}`
-	);
 
 	let existingTasks = [];
 	let nextId = 1;
+	let aiServiceResponse = null;
 
 	try {
 		// Handle file existence and overwrite/append logic
 		if (fs.existsSync(tasksPath)) {
-			if (useAppend) {
+			if (append) {
 				report(
 					`Append mode enabled. Reading existing tasks from ${tasksPath}`,
 					'info'
@@ -121,7 +121,7 @@ async function parsePRD(prdPath, tasksPath, numTasks, options = {}) {
 					);
 					existingTasks = []; // Reset if read fails
 				}
-			} else if (!useForce) {
+			} else if (!force) {
 				// Not appending and not forcing overwrite
 				const overwriteError = new Error(
 					`Output file ${tasksPath} already exists. Use --force to overwrite or --append.`
@@ -206,8 +206,8 @@ Guidelines:
 		// Call the unified AI service
 		report('Calling AI service to generate tasks from PRD...', 'info');
 
-		// Call generateObjectService with the CORRECT schema
+		// Call generateObjectService with the CORRECT schema and additional telemetry params
-		const generatedData = await generateObjectService({
+		aiServiceResponse = await generateObjectService({
 			role: 'main',
 			session: session,
 			projectRoot: projectRoot,
@@ -215,7 +215,8 @@ Guidelines:
 			objectName: 'tasks_data',
 			systemPrompt: systemPrompt,
 			prompt: userPrompt,
-			reportProgress
+			commandName: 'parse-prd',
+			outputType: isMCP ? 'mcp' : 'cli'
 		});
 
 		// Create the directory if it doesn't exist
@@ -223,12 +224,32 @@ Guidelines:
 		if (!fs.existsSync(tasksDir)) {
 			fs.mkdirSync(tasksDir, { recursive: true });
 		}
-		logFn.success('Successfully parsed PRD via AI service.'); // Assumes generateObjectService validated
+		logFn.success('Successfully parsed PRD via AI service.\n');
 
 		// Validate and Process Tasks
+		// const generatedData = aiServiceResponse?.mainResult?.object;
+
+		// Robustly get the actual AI-generated object
+		let generatedData = null;
+		if (aiServiceResponse?.mainResult) {
+			if (
+				typeof aiServiceResponse.mainResult === 'object' &&
+				aiServiceResponse.mainResult !== null &&
+				'tasks' in aiServiceResponse.mainResult
+			) {
+				// If mainResult itself is the object with a 'tasks' property
+				generatedData = aiServiceResponse.mainResult;
+			} else if (
+				typeof aiServiceResponse.mainResult.object === 'object' &&
+				aiServiceResponse.mainResult.object !== null &&
+				'tasks' in aiServiceResponse.mainResult.object
+			) {
+				// If mainResult.object is the object with a 'tasks' property
+				generatedData = aiServiceResponse.mainResult.object;
+			}
+		}
+
 		if (!generatedData || !Array.isArray(generatedData.tasks)) {
-			// This error *shouldn't* happen if generateObjectService enforced prdResponseSchema
-			// But keep it as a safeguard
 			logFn.error(
 				`Internal Error: generateObjectService returned unexpected data structure: ${JSON.stringify(generatedData)}`
 			);
@@ -265,36 +286,27 @@ Guidelines:
 				);
 		});
 
-		const allTasks = useAppend
+		const finalTasks = append
 			? [...existingTasks, ...processedNewTasks]
 			: processedNewTasks;
+		const outputData = { tasks: finalTasks };
 
-		const finalTaskData = { tasks: allTasks }; // Use the combined list
+		// Write the final tasks to the file
-
+		writeJSON(tasksPath, outputData);
-		// Write the tasks to the file
-		writeJSON(tasksPath, finalTaskData);
 		report(
-			`Successfully wrote ${allTasks.length} total tasks to ${tasksPath} (${processedNewTasks.length} new).`,
+			`Successfully ${append ? 'appended' : 'generated'} ${processedNewTasks.length} tasks in ${tasksPath}`,
 			'success'
 		);
-		report(`Tasks saved to: ${tasksPath}`, 'info');
 
-		// Generate individual task files
+		// Generate markdown task files after writing tasks.json
-		if (reportProgress && mcpLog) {
+		await generateTaskFiles(tasksPath, path.dirname(tasksPath), { mcpLog });
-			// Enable silent mode when being called from MCP server
-			enableSilentMode();
-			await generateTaskFiles(tasksPath, path.dirname(tasksPath));
-			disableSilentMode();
-		} else {
-			await generateTaskFiles(tasksPath, path.dirname(tasksPath));
-		}
 
-		// Only show success boxes for text output (CLI)
+		// Handle CLI output (e.g., success message)
 		if (outputFormat === 'text') {
 			console.log(
 				boxen(
 					chalk.green(
-						`Successfully generated ${processedNewTasks.length} new tasks. Total tasks in ${tasksPath}: ${allTasks.length}`
+						`Successfully generated ${processedNewTasks.length} new tasks. Total tasks in ${tasksPath}: ${finalTasks.length}`
 					),
 					{ padding: 1, borderColor: 'green', borderStyle: 'round' }
 				)
@@ -314,9 +326,18 @@ Guidelines:
 					}
 				)
 			);
+
+			if (aiServiceResponse && aiServiceResponse.telemetryData) {
+				displayAiUsageSummary(aiServiceResponse.telemetryData, 'cli');
+			}
 		}
 
-		return { success: true, tasks: processedNewTasks };
+		// Return telemetry data
+		return {
+			success: true,
+			tasksPath,
+			telemetryData: aiServiceResponse?.telemetryData
+		};
 	} catch (error) {
 		report(`Error parsing PRD: ${error.message}`, 'error');
 

scripts/modules/task-manager/set-task-status.js

@@ -8,6 +8,10 @@ import { validateTaskDependencies } from '../dependency-manager.js';
 import { getDebugFlag } from '../config-manager.js';
 import updateSingleTaskStatus from './update-single-task-status.js';
 import generateTaskFiles from './generate-task-files.js';
+import {
+	isValidTaskStatus,
+	TASK_STATUS_OPTIONS
+} from '../../../src/constants/task-status.js';
 
 /**
  * Set the status of a task
@@ -19,6 +23,11 @@ import generateTaskFiles from './generate-task-files.js';
  */
 async function setTaskStatus(tasksPath, taskIdInput, newStatus, options = {}) {
 	try {
+		if (!isValidTaskStatus(newStatus)) {
+			throw new Error(
+				`Error: Invalid status value: ${newStatus}. Use one of: ${TASK_STATUS_OPTIONS.join(', ')}`
+			);
+		}
 		// Determine if we're in MCP mode by checking for mcpLog
 		const isMcpMode = !!options?.mcpLog;
 

scripts/modules/task-manager/update-single-task-status.js

@@ -1,6 +1,7 @@
 import chalk from 'chalk';
 
 import { log } from '../utils.js';
+import { isValidTaskStatus } from '../../../src/constants/task-status.js';
 
 /**
  * Update the status of a single task
@@ -17,6 +18,12 @@ async function updateSingleTaskStatus(
 	data,
 	showUi = true
 ) {
+	if (!isValidTaskStatus(newStatus)) {
+		throw new Error(
+			`Error: Invalid status value: ${newStatus}. Use one of: ${TASK_STATUS_OPTIONS.join(', ')}`
+		);
+	}
+
 	// Check if it's a subtask (e.g., "1.2")
 	if (taskIdInput.includes('.')) {
 		const [parentId, subtaskId] = taskIdInput

scripts/modules/task-manager/update-subtask-by-id.js

@@ -3,12 +3,12 @@ import path from 'path';
 import chalk from 'chalk';
 import boxen from 'boxen';
 import Table from 'cli-table3';
-import { z } from 'zod';
 
 import {
 	getStatusWithColor,
 	startLoadingIndicator,
-	stopLoadingIndicator
+	stopLoadingIndicator,
+	displayAiUsageSummary
 } from '../ui.js';
 import {
 	log as consoleLog,
@@ -17,10 +17,7 @@ import {
 	truncate,
 	isSilentMode
 } from '../utils.js';
-import {
+import { generateTextService } from '../ai-services-unified.js';
-	generateObjectService,
-	generateTextService
-} from '../ai-services-unified.js';
 import { getDebugFlag } from '../config-manager.js';
 import generateTaskFiles from './generate-task-files.js';
 
@@ -64,7 +61,6 @@ async function updateSubtaskById(
 	try {
 		report('info', `Updating subtask ${subtaskId} with prompt: "${prompt}"`);
 
-		// Validate subtask ID format
 		if (
 			!subtaskId ||
 			typeof subtaskId !== 'string' ||
@@ -75,19 +71,16 @@ async function updateSubtaskById(
 			);
 		}
 
-		// Validate prompt
 		if (!prompt || typeof prompt !== 'string' || prompt.trim() === '') {
 			throw new Error(
 				'Prompt cannot be empty. Please provide context for the subtask update.'
 			);
 		}
 
-		// Validate tasks file exists
 		if (!fs.existsSync(tasksPath)) {
 			throw new Error(`Tasks file not found at path: ${tasksPath}`);
 		}
 
-		// Read the tasks file
 		const data = readJSON(tasksPath);
 		if (!data || !data.tasks) {
 			throw new Error(
@@ -95,7 +88,6 @@ async function updateSubtaskById(
 			);
 		}
 
-		// Parse parent and subtask IDs
 		const [parentIdStr, subtaskIdStr] = subtaskId.split('.');
 		const parentId = parseInt(parentIdStr, 10);
 		const subtaskIdNum = parseInt(subtaskIdStr, 10);
@@ -111,7 +103,6 @@ async function updateSubtaskById(
 			);
 		}
 
-		// Find the parent task
 		const parentTask = data.tasks.find((task) => task.id === parentId);
 		if (!parentTask) {
 			throw new Error(
@@ -119,7 +110,6 @@ async function updateSubtaskById(
 			);
 		}
 
-		// Find the subtask
 		if (!parentTask.subtasks || !Array.isArray(parentTask.subtasks)) {
 			throw new Error(`Parent task ${parentId} has no subtasks.`);
 		}
@@ -135,20 +125,7 @@ async function updateSubtaskById(
 
 		const subtask = parentTask.subtasks[subtaskIndex];
 
-		const subtaskSchema = z.object({
-			id: z.number().int().positive(),
-			title: z.string(),
-			description: z.string().optional(),
-			status: z.string(),
-			dependencies: z.array(z.union([z.string(), z.number()])).optional(),
-			priority: z.string().optional(),
-			details: z.string().optional(),
-			testStrategy: z.string().optional()
-		});
-
-		// Only show UI elements for text output (CLI)
 		if (outputFormat === 'text') {
-			// Show the subtask that will be updated
 			const table = new Table({
 				head: [
 					chalk.cyan.bold('ID'),
@@ -157,13 +134,11 @@ async function updateSubtaskById(
 				],
 				colWidths: [10, 55, 10]
 			});
-
 			table.push([
 				subtaskId,
 				truncate(subtask.title, 52),
 				getStatusWithColor(subtask.status)
 			]);
-
 			console.log(
 				boxen(chalk.white.bold(`Updating Subtask #${subtaskId}`), {
 					padding: 1,
@@ -172,10 +147,7 @@ async function updateSubtaskById(
 					margin: { top: 1, bottom: 0 }
 				})
 			);
-
 			console.log(table.toString());
-
-			// Start the loading indicator - only for text output
 			loadingIndicator = startLoadingIndicator(
 				useResearch
 					? 'Updating subtask with research...'
@@ -183,15 +155,15 @@ async function updateSubtaskById(
 			);
 		}
 
-		let parsedAIResponse;
+		let generatedContentString = '';
+		let newlyAddedSnippet = '';
+		let aiServiceResponse = null;
+
 		try {
-			// --- GET PARENT & SIBLING CONTEXT ---
 			const parentContext = {
 				id: parentTask.id,
 				title: parentTask.title
-				// Avoid sending full parent description/details unless necessary
 			};
-
 			const prevSubtask =
 				subtaskIndex > 0
 					? {
@@ -200,7 +172,6 @@ async function updateSubtaskById(
 							status: parentTask.subtasks[subtaskIndex - 1].status
 						}
 					: null;
-
 			const nextSubtask =
 				subtaskIndex < parentTask.subtasks.length - 1
 					? {
@@ -214,154 +185,123 @@ async function updateSubtaskById(
 Parent Task: ${JSON.stringify(parentContext)}
 ${prevSubtask ? `Previous Subtask: ${JSON.stringify(prevSubtask)}` : ''}
 ${nextSubtask ? `Next Subtask: ${JSON.stringify(nextSubtask)}` : ''}
+Current Subtask Details (for context only):\n${subtask.details || '(No existing details)'}
 `;
 
-			const systemPrompt = `You are an AI assistant updating a parent task's subtask. This subtask will be part of a larger parent task and will be used to direct AI agents to complete the subtask. Your goal is to GENERATE new, relevant information based on the user's request (which may be high-level, mid-level or low-level) and APPEND it to the existing subtask 'details' field, wrapped in specific XML-like tags with an ISO 8601 timestamp. Intelligently determine the level of detail to include based on the user's request. Some requests are meant simply to update the subtask with some mid-implementation details, while others are meant to update the subtask with a detailed plan or strategy.
+			const systemPrompt = `You are an AI assistant helping to update a subtask. You will be provided with the subtask's existing details, context about its parent and sibling tasks, and a user request string.
 
-Context Provided:
+Your Goal: Based *only* on the user's request and all the provided context (including existing details if relevant to the request), GENERATE the new text content that should be added to the subtask's details.
-- The current subtask object.
+Focus *only* on generating the substance of the update.
-- Basic info about the parent task (ID, title).
-- Basic info about the immediately preceding subtask (ID, title, status), if it exists.
-- Basic info about the immediately succeeding subtask (ID, title, status), if it exists.
-- A user request string.
 
-Guidelines:
+Output Requirements:
-1. Analyze the user request considering the provided subtask details AND the context of the parent and sibling tasks.
+1. Return *only* the newly generated text content as a plain string. Do NOT return a JSON object or any other structured data.
-2. GENERATE new, relevant text content that should be added to the 'details' field. Focus *only* on the substance of the update based on the user request and context. Do NOT add timestamps or any special formatting yourself. Avoid over-engineering the details, provide .
+2. Your string response should NOT include any of the subtask's original details, unless the user's request explicitly asks to rephrase, summarize, or directly modify existing text.
-3. Update the 'details' field in the subtask object with the GENERATED text content. It's okay if this overwrites previous details in the object you return, as the calling code will handle the final appending.
+3. Do NOT include any timestamps, XML-like tags, markdown, or any other special formatting in your string response.
-4. Return the *entire* updated subtask object (with your generated content in the 'details' field) as a valid JSON object conforming to the provided schema. Do NOT return explanations or markdown formatting.`;
+4. Ensure the generated text is concise yet complete for the update based on the user request. Avoid conversational fillers or explanations about what you are doing (e.g., do not start with "Okay, here's the update...").`;
 
-			const subtaskDataString = JSON.stringify(subtask, null, 2);
+			// Pass the existing subtask.details in the user prompt for the AI's context.
-			// Updated user prompt including context
+			const userPrompt = `Task Context:\n${contextString}\n\nUser Request: "${prompt}"\n\nBased on the User Request and all the Task Context (including current subtask details provided above), what is the new information or text that should be appended to this subtask's details? Return ONLY this new text as a plain string.`;
-			const userPrompt = `Task Context:\n${contextString}\nCurrent Subtask:\n${subtaskDataString}\n\nUser Request: "${prompt}"\n\nPlease GENERATE new, relevant text content for the 'details' field based on the user request and the provided context. Return the entire updated subtask object as a valid JSON object matching the schema, with the newly generated text placed in the 'details' field.`;
-			// --- END UPDATED PROMPTS ---
 
-			// Call Unified AI Service using generateObjectService
 			const role = useResearch ? 'research' : 'main';
-			report('info', `Using AI object service with role: ${role}`);
+			report('info', `Using AI text service with role: ${role}`);
 
-			parsedAIResponse = await generateObjectService({
+			aiServiceResponse = await generateTextService({
 				prompt: userPrompt,
 				systemPrompt: systemPrompt,
-				schema: subtaskSchema,
-				objectName: 'updatedSubtask',
 				role,
 				session,
 				projectRoot,
-				maxRetries: 2
+				maxRetries: 2,
+				commandName: 'update-subtask',
+				outputType: isMCP ? 'mcp' : 'cli'
 			});
-			report(
+
-				'success',
+			if (
-				'Successfully received object response from AI service'
+				aiServiceResponse &&
-			);
+				aiServiceResponse.mainResult &&
+				typeof aiServiceResponse.mainResult === 'string'
+			) {
+				generatedContentString = aiServiceResponse.mainResult;
+			} else {
+				generatedContentString = '';
+				report(
+					'warn',
+					'AI service response did not contain expected text string.'
+				);
+			}
 
 			if (outputFormat === 'text' && loadingIndicator) {
 				stopLoadingIndicator(loadingIndicator);
 				loadingIndicator = null;
 			}
-
-			if (!parsedAIResponse || typeof parsedAIResponse !== 'object') {
-				throw new Error('AI did not return a valid object.');
-			}
-
-			report(
-				'success',
-				`Successfully generated object using AI role: ${role}.`
-			);
 		} catch (aiError) {
 			report('error', `AI service call failed: ${aiError.message}`);
 			if (outputFormat === 'text' && loadingIndicator) {
-				stopLoadingIndicator(loadingIndicator); // Ensure stop on error
+				stopLoadingIndicator(loadingIndicator);
 				loadingIndicator = null;
 			}
 			throw aiError;
 		}
 
-		// --- TIMESTAMP & FORMATTING LOGIC (Handled Locally) ---
+		if (generatedContentString && generatedContentString.trim()) {
-		// Extract only the generated content from the AI's response details field.
+			// Check if the string is not empty
-		const generatedContent = parsedAIResponse.details || ''; // Default to empty string
+			const timestamp = new Date().toISOString();
+			const formattedBlock = `<info added on ${timestamp}>\n${generatedContentString.trim()}\n</info added on ${timestamp}>`;
+			newlyAddedSnippet = formattedBlock; // <--- ADD THIS LINE: Store for display
 
-		if (generatedContent.trim()) {
-			// Generate timestamp locally
-			const timestamp = new Date().toISOString(); // <<< Local Timestamp
-
-			// Format the content with XML-like tags and timestamp LOCALLY
-			const formattedBlock = `<info added on ${timestamp}>\n${generatedContent.trim()}\n</info added on ${timestamp}>`; // <<< Local Formatting
-
-			// Append the formatted block to the *original* subtask details
 			subtask.details =
-				(subtask.details ? subtask.details + '\n' : '') + formattedBlock; // <<< Local Appending
+				(subtask.details ? subtask.details + '\n' : '') + formattedBlock;
-			report(
-				'info',
-				'Appended timestamped, formatted block with AI-generated content to subtask.details.'
-			);
 		} else {
 			report(
 				'warn',
-				'AI response object did not contain generated content in the "details" field. Original details remain unchanged.'
+				'AI response was empty or whitespace after trimming. Original details remain unchanged.'
 			);
+			newlyAddedSnippet = 'No new details were added by the AI.';
 		}
-		// --- END TIMESTAMP & FORMATTING LOGIC ---
 
-		// Get a reference to the subtask *after* its details have been updated
+		const updatedSubtask = parentTask.subtasks[subtaskIndex];
-		const updatedSubtask = parentTask.subtasks[subtaskIndex]; // subtask === updatedSubtask now
 
-		report('info', 'Updated subtask details locally after AI generation.');
-		// --- END UPDATE SUBTASK ---
-
-		// Only show debug info for text output (CLI)
 		if (outputFormat === 'text' && getDebugFlag(session)) {
 			console.log(
 				'>>> DEBUG: Subtask details AFTER AI update:',
-				updatedSubtask.details // Use updatedSubtask
+				updatedSubtask.details
 			);
 		}
 
-		// Description update logic (keeping as is for now)
 		if (updatedSubtask.description) {
-			// Use updatedSubtask
 			if (prompt.length < 100) {
 				if (outputFormat === 'text' && getDebugFlag(session)) {
 					console.log(
 						'>>> DEBUG: Subtask description BEFORE append:',
-						updatedSubtask.description // Use updatedSubtask
+						updatedSubtask.description
 					);
 				}
-				updatedSubtask.description += ` [Updated: ${new Date().toLocaleDateString()}]`; // Use updatedSubtask
+				updatedSubtask.description += ` [Updated: ${new Date().toLocaleDateString()}]`;
 				if (outputFormat === 'text' && getDebugFlag(session)) {
 					console.log(
 						'>>> DEBUG: Subtask description AFTER append:',
-						updatedSubtask.description // Use updatedSubtask
+						updatedSubtask.description
 					);
 				}
 			}
 		}
 
-		// Only show debug info for text output (CLI)
 		if (outputFormat === 'text' && getDebugFlag(session)) {
 			console.log('>>> DEBUG: About to call writeJSON with updated data...');
 		}
-
-		// Write the updated tasks to the file (parentTask already contains the updated subtask)
 		writeJSON(tasksPath, data);
-
-		// Only show debug info for text output (CLI)
 		if (outputFormat === 'text' && getDebugFlag(session)) {
 			console.log('>>> DEBUG: writeJSON call completed.');
 		}
 
 		report('success', `Successfully updated subtask ${subtaskId}`);
-
-		// Generate individual task files
 		await generateTaskFiles(tasksPath, path.dirname(tasksPath));
 
-		// Stop indicator before final console output - only for text output (CLI)
 		if (outputFormat === 'text') {
 			if (loadingIndicator) {
 				stopLoadingIndicator(loadingIndicator);
 				loadingIndicator = null;
 			}
-
 			console.log(
 				boxen(
 					chalk.green(`Successfully updated subtask #${subtaskId}`) +
@@ -370,31 +310,30 @@ Guidelines:
 						' ' +
 						updatedSubtask.title +
 						'\n\n' +
-						// Update the display to show the new details field
+						chalk.white.bold('Newly Added Snippet:') +
-						chalk.white.bold('Updated Details:') +
 						'\n' +
-						chalk.white(truncate(updatedSubtask.details || '', 500, true)), // Use updatedSubtask
+						chalk.white(newlyAddedSnippet),
 					{ padding: 1, borderColor: 'green', borderStyle: 'round' }
 				)
 			);
 		}
 
-		return updatedSubtask; // Return the modified subtask object
+		if (outputFormat === 'text' && aiServiceResponse.telemetryData) {
+			displayAiUsageSummary(aiServiceResponse.telemetryData, 'cli');
+		}
+
+		return {
+			updatedSubtask: updatedSubtask,
+			telemetryData: aiServiceResponse.telemetryData
+		};
 	} catch (error) {
-		// Outer catch block handles final errors after loop/attempts
-		// Stop indicator on error - only for text output (CLI)
 		if (outputFormat === 'text' && loadingIndicator) {
 			stopLoadingIndicator(loadingIndicator);
 			loadingIndicator = null;
 		}
-
 		report('error', `Error updating subtask: ${error.message}`);
-
-		// Only show error UI for text output (CLI)
 		if (outputFormat === 'text') {
 			console.error(chalk.red(`Error: ${error.message}`));
-
-			// Provide helpful error messages based on error type
 			if (error.message?.includes('ANTHROPIC_API_KEY')) {
 				console.log(
 					chalk.yellow('\nTo fix this issue, set your Anthropic API key:')
@@ -409,7 +348,6 @@ Guidelines:
 					'  2. Or run without the research flag: task-master update-subtask --id=<id> --prompt="..."'
 				);
 			} else if (error.message?.includes('overloaded')) {
-				// Catch final overload error
 				console.log(
 					chalk.yellow(
 						'\nAI model overloaded, and fallback failed or was unavailable:'
@@ -417,7 +355,6 @@ Guidelines:
 				);
 				console.log('  1. Try again in a few minutes.');
 				console.log('  2. Ensure PERPLEXITY_API_KEY is set for fallback.');
-				console.log('  3. Consider breaking your prompt into smaller updates.');
 			} else if (error.message?.includes('not found')) {
 				console.log(chalk.yellow('\nTo fix this issue:'));
 				console.log(
@@ -426,22 +363,22 @@ Guidelines:
 				console.log(
 					'  2. Use a valid subtask ID with the --id parameter in format "parentId.subtaskId"'
 				);
-			} else if (error.message?.includes('empty stream response')) {
+			} else if (
+				error.message?.includes('empty stream response') ||
+				error.message?.includes('AI did not return a valid text string')
+			) {
 				console.log(
 					chalk.yellow(
-						'\nThe AI model returned an empty response. This might be due to the prompt or API issues. Try rephrasing or trying again later.'
+						'\nThe AI model returned an empty or invalid response. This might be due to the prompt or API issues. Try rephrasing or trying again later.'
 					)
 				);
 			}
-
 			if (getDebugFlag(session)) {
-				// Use getter
 				console.error(error);
 			}
 		} else {
-			throw error; // Re-throw for JSON output
+			throw error;
 		}
-
 		return null;
 	}
 }

scripts/modules/task-manager/update-task-by-id.js

@@ -16,7 +16,8 @@ import {
 import {
 	getStatusWithColor,
 	startLoadingIndicator,
-	stopLoadingIndicator
+	stopLoadingIndicator,
+	displayAiUsageSummary
 } from '../ui.js';
 
 import { generateTextService } from '../ai-services-unified.js';
@@ -94,10 +95,6 @@ function parseUpdatedTaskFromText(text, expectedTaskId, logFn, isMCP) {
 			// It worked! Use this as the primary cleaned response.
 			cleanedResponse = potentialJsonFromBraces;
 			parseMethodUsed = 'braces';
-			report(
-				'info',
-				'Successfully parsed JSON content extracted between first { and last }.'
-			);
 		} catch (e) {
 			report(
 				'info',
@@ -376,29 +373,125 @@ The changes described in the prompt should be thoughtfully applied to make the t
 		const userPrompt = `Here is the task to update:\n${taskDataString}\n\nPlease update this task based on the following new context:\n${prompt}\n\nIMPORTANT: In the task JSON above, any subtasks with "status": "done" or "status": "completed" should be preserved exactly as is. Build your changes around these completed items.\n\nReturn only the updated task as a valid JSON object.`;
 		// --- End Build Prompts ---
 
-		let updatedTask;
 		let loadingIndicator = null;
-		if (outputFormat === 'text') {
+		let aiServiceResponse = null;
+
+		if (!isMCP && outputFormat === 'text') {
 			loadingIndicator = startLoadingIndicator(
 				useResearch ? 'Updating task with research...\n' : 'Updating task...\n'
 			);
 		}
 
-		let responseText = '';
 		try {
-			// --- Call Unified AI Service (generateTextService) ---
+			const serviceRole = useResearch ? 'research' : 'main';
-			const role = useResearch ? 'research' : 'main';
+			aiServiceResponse = await generateTextService({
-			report('info', `Using AI service with role: ${role}`);
+				role: serviceRole,
-
+				session: session,
-			responseText = await generateTextService({
+				projectRoot: projectRoot,
-				prompt: userPrompt,
 				systemPrompt: systemPrompt,
-				role,
+				prompt: userPrompt,
-				session,
+				commandName: 'update-task',
-				projectRoot
+				outputType: isMCP ? 'mcp' : 'cli'
 			});
-			report('success', 'Successfully received text response from AI service');
+
-			// --- End AI Service Call ---
+			if (loadingIndicator)
+				stopLoadingIndicator(loadingIndicator, 'AI update complete.');
+
+			// Use mainResult (text) for parsing
+			const updatedTask = parseUpdatedTaskFromText(
+				aiServiceResponse.mainResult,
+				taskId,
+				logFn,
+				isMCP
+			);
+
+			// --- Task Validation/Correction (Keep existing logic) ---
+			if (!updatedTask || typeof updatedTask !== 'object')
+				throw new Error('Received invalid task object from AI.');
+			if (!updatedTask.title || !updatedTask.description)
+				throw new Error('Updated task missing required fields.');
+			// Preserve ID if AI changed it
+			if (updatedTask.id !== taskId) {
+				report('warn', `AI changed task ID. Restoring original ID ${taskId}.`);
+				updatedTask.id = taskId;
+			}
+			// Preserve status if AI changed it
+			if (
+				updatedTask.status !== taskToUpdate.status &&
+				!prompt.toLowerCase().includes('status')
+			) {
+				report(
+					'warn',
+					`AI changed task status. Restoring original status '${taskToUpdate.status}'.`
+				);
+				updatedTask.status = taskToUpdate.status;
+			}
+			// Preserve completed subtasks (Keep existing logic)
+			if (taskToUpdate.subtasks?.length > 0) {
+				if (!updatedTask.subtasks) {
+					report(
+						'warn',
+						'Subtasks removed by AI. Restoring original subtasks.'
+					);
+					updatedTask.subtasks = taskToUpdate.subtasks;
+				} else {
+					const completedOriginal = taskToUpdate.subtasks.filter(
+						(st) => st.status === 'done' || st.status === 'completed'
+					);
+					completedOriginal.forEach((compSub) => {
+						const updatedSub = updatedTask.subtasks.find(
+							(st) => st.id === compSub.id
+						);
+						if (
+							!updatedSub ||
+							JSON.stringify(updatedSub) !== JSON.stringify(compSub)
+						) {
+							report(
+								'warn',
+								`Completed subtask ${compSub.id} was modified or removed. Restoring.`
+							);
+							// Remove potentially modified version
+							updatedTask.subtasks = updatedTask.subtasks.filter(
+								(st) => st.id !== compSub.id
+							);
+							// Add back original
+							updatedTask.subtasks.push(compSub);
+						}
+					});
+					// Deduplicate just in case
+					const subtaskIds = new Set();
+					updatedTask.subtasks = updatedTask.subtasks.filter((st) => {
+						if (!subtaskIds.has(st.id)) {
+							subtaskIds.add(st.id);
+							return true;
+						}
+						report('warn', `Duplicate subtask ID ${st.id} removed.`);
+						return false;
+					});
+				}
+			}
+			// --- End Task Validation/Correction ---
+
+			// --- Update Task Data (Keep existing) ---
+			data.tasks[taskIndex] = updatedTask;
+			// --- End Update Task Data ---
+
+			// --- Write File and Generate (Unchanged) ---
+			writeJSON(tasksPath, data);
+			report('success', `Successfully updated task ${taskId}`);
+			await generateTaskFiles(tasksPath, path.dirname(tasksPath));
+			// --- End Write File ---
+
+			// --- Display CLI Telemetry ---
+			if (outputFormat === 'text' && aiServiceResponse.telemetryData) {
+				displayAiUsageSummary(aiServiceResponse.telemetryData, 'cli'); // <<< ADD display
+			}
+
+			// --- Return Success with Telemetry ---
+			return {
+				updatedTask: updatedTask, // Return the updated task object
+				telemetryData: aiServiceResponse.telemetryData // <<< ADD telemetryData
+			};
 		} catch (error) {
 			// Catch errors from generateTextService
 			if (loadingIndicator) stopLoadingIndicator(loadingIndicator);
@@ -407,114 +500,7 @@ The changes described in the prompt should be thoughtfully applied to make the t
 				report('error', 'Please ensure API keys are configured correctly.');
 			}
 			throw error; // Re-throw error
-		} finally {
-			if (loadingIndicator) stopLoadingIndicator(loadingIndicator);
 		}
-
-		// --- Parse and Validate Response ---
-		try {
-			// Pass logFn and isMCP flag to the parser
-			updatedTask = parseUpdatedTaskFromText(
-				responseText,
-				taskId,
-				logFn,
-				isMCP
-			);
-		} catch (parseError) {
-			report(
-				'error',
-				`Failed to parse updated task from AI response: ${parseError.message}`
-			);
-			if (getDebugFlag(session)) {
-				report('error', `Raw AI Response:\n${responseText}`);
-			}
-			throw new Error(
-				`Failed to parse valid updated task from AI response: ${parseError.message}`
-			);
-		}
-		// --- End Parse/Validate ---
-
-		// --- Task Validation/Correction (Keep existing logic) ---
-		if (!updatedTask || typeof updatedTask !== 'object')
-			throw new Error('Received invalid task object from AI.');
-		if (!updatedTask.title || !updatedTask.description)
-			throw new Error('Updated task missing required fields.');
-		// Preserve ID if AI changed it
-		if (updatedTask.id !== taskId) {
-			report('warn', `AI changed task ID. Restoring original ID ${taskId}.`);
-			updatedTask.id = taskId;
-		}
-		// Preserve status if AI changed it
-		if (
-			updatedTask.status !== taskToUpdate.status &&
-			!prompt.toLowerCase().includes('status')
-		) {
-			report(
-				'warn',
-				`AI changed task status. Restoring original status '${taskToUpdate.status}'.`
-			);
-			updatedTask.status = taskToUpdate.status;
-		}
-		// Preserve completed subtasks (Keep existing logic)
-		if (taskToUpdate.subtasks?.length > 0) {
-			if (!updatedTask.subtasks) {
-				report('warn', 'Subtasks removed by AI. Restoring original subtasks.');
-				updatedTask.subtasks = taskToUpdate.subtasks;
-			} else {
-				const completedOriginal = taskToUpdate.subtasks.filter(
-					(st) => st.status === 'done' || st.status === 'completed'
-				);
-				completedOriginal.forEach((compSub) => {
-					const updatedSub = updatedTask.subtasks.find(
-						(st) => st.id === compSub.id
-					);
-					if (
-						!updatedSub ||
-						JSON.stringify(updatedSub) !== JSON.stringify(compSub)
-					) {
-						report(
-							'warn',
-							`Completed subtask ${compSub.id} was modified or removed. Restoring.`
-						);
-						// Remove potentially modified version
-						updatedTask.subtasks = updatedTask.subtasks.filter(
-							(st) => st.id !== compSub.id
-						);
-						// Add back original
-						updatedTask.subtasks.push(compSub);
-					}
-				});
-				// Deduplicate just in case
-				const subtaskIds = new Set();
-				updatedTask.subtasks = updatedTask.subtasks.filter((st) => {
-					if (!subtaskIds.has(st.id)) {
-						subtaskIds.add(st.id);
-						return true;
-					}
-					report('warn', `Duplicate subtask ID ${st.id} removed.`);
-					return false;
-				});
-			}
-		}
-		// --- End Task Validation/Correction ---
-
-		// --- Update Task Data (Keep existing) ---
-		data.tasks[taskIndex] = updatedTask;
-		// --- End Update Task Data ---
-
-		// --- Write File and Generate (Keep existing) ---
-		writeJSON(tasksPath, data);
-		report('success', `Successfully updated task ${taskId}`);
-		await generateTaskFiles(tasksPath, path.dirname(tasksPath));
-		// --- End Write File ---
-
-		// --- Final CLI Output (Keep existing) ---
-		if (outputFormat === 'text') {
-			/* ... success boxen ... */
-		}
-		// --- End Final CLI Output ---
-
-		return updatedTask; // Return the updated task
 	} catch (error) {
 		// General error catch
 		// --- General Error Handling (Keep existing) ---

scripts/modules/task-manager/update-tasks.js

@@ -15,7 +15,8 @@ import {
 import {
 	getStatusWithColor,
 	startLoadingIndicator,
-	stopLoadingIndicator
+	stopLoadingIndicator,
+	displayAiUsageSummary
 } from '../ui.js';
 
 import { getDebugFlag } from '../config-manager.js';
@@ -93,10 +94,6 @@ function parseUpdatedTasksFromText(text, expectedCount, logFn, isMCP) {
 			// It worked! Use this as the primary cleaned response.
 			cleanedResponse = potentialJsonFromArray;
 			parseMethodUsed = 'brackets';
-			report(
-				'info',
-				'Successfully parsed JSON content extracted between first [ and last ].'
-			);
 		} catch (e) {
 			report(
 				'info',
@@ -350,31 +347,100 @@ The changes described in the prompt should be applied to ALL tasks in the list.`
 		const userPrompt = `Here are the tasks to update:\n${taskDataString}\n\nPlease update these tasks based on the following new context:\n${prompt}\n\nIMPORTANT: In the tasks JSON above, any subtasks with "status": "done" or "status": "completed" should be preserved exactly as is. Build your changes around these completed items.\n\nReturn only the updated tasks as a valid JSON array.`;
 		// --- End Build Prompts ---
 
+		// --- AI Call ---
 		let loadingIndicator = null;
-		if (outputFormat === 'text') {
+		let aiServiceResponse = null;
-			loadingIndicator = startLoadingIndicator('Updating tasks...\n');
+
+		if (!isMCP && outputFormat === 'text') {
+			loadingIndicator = startLoadingIndicator('Updating tasks with AI...\n');
 		}
 
-		let responseText = '';
-		let updatedTasks;
-
 		try {
-			// --- Call Unified AI Service ---
+			// Determine role based on research flag
-			const role = useResearch ? 'research' : 'main';
+			const serviceRole = useResearch ? 'research' : 'main';
-			if (isMCP) logFn.info(`Using AI service with role: ${role}`);
-			else logFn('info', `Using AI service with role: ${role}`);
 
-			responseText = await generateTextService({
+			// Call the unified AI service
-				prompt: userPrompt,
+			aiServiceResponse = await generateTextService({
+				role: serviceRole,
+				session: session,
+				projectRoot: projectRoot,
 				systemPrompt: systemPrompt,
-				role,
+				prompt: userPrompt,
-				session,
+				commandName: 'update-tasks',
-				projectRoot
+				outputType: isMCP ? 'mcp' : 'cli'
 			});
-			if (isMCP) logFn.info('Successfully received text response');
+
+			if (loadingIndicator)
+				stopLoadingIndicator(loadingIndicator, 'AI update complete.');
+
+			// Use the mainResult (text) for parsing
+			const parsedUpdatedTasks = parseUpdatedTasksFromText(
+				aiServiceResponse.mainResult,
+				tasksToUpdate.length,
+				logFn,
+				isMCP
+			);
+
+			// --- Update Tasks Data (Unchanged) ---
+			if (!Array.isArray(parsedUpdatedTasks)) {
+				// Should be caught by parser, but extra check
+				throw new Error(
+					'Parsed AI response for updated tasks was not an array.'
+				);
+			}
+			if (isMCP)
+				logFn.info(
+					`Received ${parsedUpdatedTasks.length} updated tasks from AI.`
+				);
 			else
-				logFn('success', 'Successfully received text response via AI service');
+				logFn(
-			// --- End AI Service Call ---
+					'info',
+					`Received ${parsedUpdatedTasks.length} updated tasks from AI.`
+				);
+			// Create a map for efficient lookup
+			const updatedTasksMap = new Map(
+				parsedUpdatedTasks.map((task) => [task.id, task])
+			);
+
+			let actualUpdateCount = 0;
+			data.tasks.forEach((task, index) => {
+				if (updatedTasksMap.has(task.id)) {
+					// Only update if the task was part of the set sent to AI
+					data.tasks[index] = updatedTasksMap.get(task.id);
+					actualUpdateCount++;
+				}
+			});
+			if (isMCP)
+				logFn.info(
+					`Applied updates to ${actualUpdateCount} tasks in the dataset.`
+				);
+			else
+				logFn(
+					'info',
+					`Applied updates to ${actualUpdateCount} tasks in the dataset.`
+				);
+
+			writeJSON(tasksPath, data);
+			if (isMCP)
+				logFn.info(
+					`Successfully updated ${actualUpdateCount} tasks in ${tasksPath}`
+				);
+			else
+				logFn(
+					'success',
+					`Successfully updated ${actualUpdateCount} tasks in ${tasksPath}`
+				);
+			await generateTaskFiles(tasksPath, path.dirname(tasksPath));
+
+			if (outputFormat === 'text' && aiServiceResponse.telemetryData) {
+				displayAiUsageSummary(aiServiceResponse.telemetryData, 'cli');
+			}
+
+			return {
+				success: true,
+				updatedTasks: parsedUpdatedTasks,
+				telemetryData: aiServiceResponse.telemetryData
+			};
 		} catch (error) {
 			if (loadingIndicator) stopLoadingIndicator(loadingIndicator);
 			if (isMCP) logFn.error(`Error during AI service call: ${error.message}`);
@@ -390,98 +456,10 @@ The changes described in the prompt should be applied to ALL tasks in the list.`
 						'Please ensure API keys are configured correctly in .env or mcp.json.'
 					);
 			}
-			throw error; // Re-throw error
+			throw error;
 		} finally {
 			if (loadingIndicator) stopLoadingIndicator(loadingIndicator);
 		}
-
-		// --- Parse and Validate Response ---
-		try {
-			updatedTasks = parseUpdatedTasksFromText(
-				responseText,
-				tasksToUpdate.length,
-				logFn,
-				isMCP
-			);
-		} catch (parseError) {
-			if (isMCP)
-				logFn.error(
-					`Failed to parse updated tasks from AI response: ${parseError.message}`
-				);
-			else
-				logFn(
-					'error',
-					`Failed to parse updated tasks from AI response: ${parseError.message}`
-				);
-			if (getDebugFlag(session)) {
-				if (isMCP) logFn.error(`Raw AI Response:\n${responseText}`);
-				else logFn('error', `Raw AI Response:\n${responseText}`);
-			}
-			throw new Error(
-				`Failed to parse valid updated tasks from AI response: ${parseError.message}`
-			);
-		}
-		// --- End Parse/Validate ---
-
-		// --- Update Tasks Data (Unchanged) ---
-		if (!Array.isArray(updatedTasks)) {
-			// Should be caught by parser, but extra check
-			throw new Error('Parsed AI response for updated tasks was not an array.');
-		}
-		if (isMCP)
-			logFn.info(`Received ${updatedTasks.length} updated tasks from AI.`);
-		else
-			logFn('info', `Received ${updatedTasks.length} updated tasks from AI.`);
-		// Create a map for efficient lookup
-		const updatedTasksMap = new Map(
-			updatedTasks.map((task) => [task.id, task])
-		);
-
-		// Iterate through the original data and update based on the map
-		let actualUpdateCount = 0;
-		data.tasks.forEach((task, index) => {
-			if (updatedTasksMap.has(task.id)) {
-				// Only update if the task was part of the set sent to AI
-				data.tasks[index] = updatedTasksMap.get(task.id);
-				actualUpdateCount++;
-			}
-		});
-		if (isMCP)
-			logFn.info(
-				`Applied updates to ${actualUpdateCount} tasks in the dataset.`
-			);
-		else
-			logFn(
-				'info',
-				`Applied updates to ${actualUpdateCount} tasks in the dataset.`
-			);
-		// --- End Update Tasks Data ---
-
-		// --- Write File and Generate (Unchanged) ---
-		writeJSON(tasksPath, data);
-		if (isMCP)
-			logFn.info(
-				`Successfully updated ${actualUpdateCount} tasks in ${tasksPath}`
-			);
-		else
-			logFn(
-				'success',
-				`Successfully updated ${actualUpdateCount} tasks in ${tasksPath}`
-			);
-		await generateTaskFiles(tasksPath, path.dirname(tasksPath));
-		// --- End Write File ---
-
-		// --- Final CLI Output (Unchanged) ---
-		if (outputFormat === 'text') {
-			console.log(
-				boxen(chalk.green(`Successfully updated ${actualUpdateCount} tasks`), {
-					padding: 1,
-					borderColor: 'green',
-					borderStyle: 'round'
-				})
-			);
-		}
-		// --- End Final CLI Output ---
 	} catch (error) {
 		// --- General Error Handling (Unchanged) ---
 		if (isMCP) logFn.error(`Error updating tasks: ${error.message}`);

scripts/modules/ui.js

@@ -16,10 +16,15 @@ import {
 	truncate,
 	isSilentMode
 } from './utils.js';
-import path from 'path';
 import fs from 'fs';
-import { findNextTask, analyzeTaskComplexity } from './task-manager.js';
+import {
+	findNextTask,
+	analyzeTaskComplexity,
+	readComplexityReport
+} from './task-manager.js';
 import { getProjectName, getDefaultSubtasks } from './config-manager.js';
+import { TASK_STATUS_OPTIONS } from '../../src/constants/task-status.js';
+import { getTaskMasterVersion } from '../../src/utils/getVersion.js';
 
 // Create a color gradient for the banner
 const coolGradient = gradient(['#00b4d8', '#0077b6', '#03045e']);
@@ -46,17 +51,7 @@ function displayBanner() {
 	);
 
 	// Read version directly from package.json
-	let version = 'unknown'; // Initialize with a default
+	const version = getTaskMasterVersion();
-	try {
-		const packageJsonPath = path.join(process.cwd(), 'package.json');
-		if (fs.existsSync(packageJsonPath)) {
-			const packageJson = JSON.parse(fs.readFileSync(packageJsonPath, 'utf8'));
-			version = packageJson.version;
-		}
-	} catch (error) {
-		// Silently fall back to default version
-		log('warn', 'Could not read package.json for version info.');
-	}
 
 	console.log(
 		boxen(
@@ -273,12 +268,14 @@ function getStatusWithColor(status, forTable = false) {
  * @param {Array} dependencies - Array of dependency IDs
  * @param {Array} allTasks - Array of all tasks
  * @param {boolean} forConsole - Whether the output is for console display
+ * @param {Object|null} complexityReport - Optional pre-loaded complexity report
  * @returns {string} Formatted dependencies string
  */
 function formatDependenciesWithStatus(
 	dependencies,
 	allTasks,
-	forConsole = false
+	forConsole = false,
+	complexityReport = null // Add complexityReport parameter
 ) {
 	if (
 		!dependencies ||
@@ -342,7 +339,11 @@ function formatDependenciesWithStatus(
 			typeof depId === 'string' ? parseInt(depId, 10) : depId;
 
 		// Look up the task using the numeric ID
-		const depTaskResult = findTaskById(allTasks, numericDepId);
+		const depTaskResult = findTaskById(
+			allTasks,
+			numericDepId,
+			complexityReport
+		);
 		const depTask = depTaskResult.task; // Access the task object from the result
 
 		if (!depTask) {
@@ -458,7 +459,7 @@ function displayHelp() {
 				{
 					name: 'set-status',
 					args: '--id=<id> --status=<status>',
-					desc: 'Update task status (done, pending, etc.)'
+					desc: `Update task status (${TASK_STATUS_OPTIONS.join(', ')})`
 				},
 				{
 					name: 'update',
@@ -761,7 +762,7 @@ function truncateString(str, maxLength) {
  * Display the next task to work on
  * @param {string} tasksPath - Path to the tasks.json file
  */
-async function displayNextTask(tasksPath) {
+async function displayNextTask(tasksPath, complexityReportPath = null) {
 	displayBanner();
 
 	// Read the tasks file
@@ -771,8 +772,11 @@ async function displayNextTask(tasksPath) {
 		process.exit(1);
 	}
 
+	// Read complexity report once
+	const complexityReport = readComplexityReport(complexityReportPath);
+
 	// Find the next task
-	const nextTask = findNextTask(data.tasks);
+	const nextTask = findNextTask(data.tasks, complexityReport);
 
 	if (!nextTask) {
 		console.log(
@@ -809,12 +813,7 @@ async function displayNextTask(tasksPath) {
 			'padding-bottom': 0,
 			compact: true
 		},
-		chars: {
+		chars: { mid: '', 'left-mid': '', 'mid-mid': '', 'right-mid': '' },
-			mid: '',
-			'left-mid': '',
-			'mid-mid': '',
-			'right-mid': ''
-		},
 		colWidths: [15, Math.min(75, process.stdout.columns - 20 || 60)],
 		wordWrap: true
 	});
@@ -838,7 +837,18 @@ async function displayNextTask(tasksPath) {
 		],
 		[
 			chalk.cyan.bold('Dependencies:'),
-			formatDependenciesWithStatus(nextTask.dependencies, data.tasks, true)
+			formatDependenciesWithStatus(
+				nextTask.dependencies,
+				data.tasks,
+				true,
+				complexityReport
+			)
+		],
+		[
+			chalk.cyan.bold('Complexity:'),
+			nextTask.complexityScore
+				? getComplexityWithColor(nextTask.complexityScore)
+				: chalk.gray('N/A')
 		],
 		[chalk.cyan.bold('Description:'), nextTask.description]
 	);
@@ -860,8 +870,11 @@ async function displayNextTask(tasksPath) {
 		);
 	}
 
-	// Show subtasks if they exist
+	// Determine if the nextTask is a subtask
-	if (nextTask.subtasks && nextTask.subtasks.length > 0) {
+	const isSubtask = !!nextTask.parentId;
+
+	// Show subtasks if they exist (only for parent tasks)
+	if (!isSubtask && nextTask.subtasks && nextTask.subtasks.length > 0) {
 		console.log(
 			boxen(chalk.white.bold('Subtasks'), {
 				padding: { top: 0, bottom: 0, left: 1, right: 1 },
@@ -902,12 +915,7 @@ async function displayNextTask(tasksPath) {
 				'padding-bottom': 0,
 				compact: true
 			},
-			chars: {
+			chars: { mid: '', 'left-mid': '', 'mid-mid': '', 'right-mid': '' },
-				mid: '',
-				'left-mid': '',
-				'mid-mid': '',
-				'right-mid': ''
-			},
 			wordWrap: true
 		});
 
@@ -966,8 +974,10 @@ async function displayNextTask(tasksPath) {
 		});
 
 		console.log(subtaskTable.toString());
-	} else {
+	}
-		// Suggest expanding if no subtasks
+
+	// Suggest expanding if no subtasks (only for parent tasks without subtasks)
+	if (!isSubtask && (!nextTask.subtasks || nextTask.subtasks.length === 0)) {
 		console.log(
 			boxen(
 				chalk.yellow('No subtasks found. Consider breaking down this task:') +
@@ -986,22 +996,30 @@ async function displayNextTask(tasksPath) {
 	}
 
 	// Show action suggestions
+	let suggestedActionsContent = chalk.white.bold('Suggested Actions:') + '\n';
+	if (isSubtask) {
+		// Suggested actions for a subtask
+		suggestedActionsContent +=
+			`${chalk.cyan('1.')} Mark as in-progress: ${chalk.yellow(`task-master set-status --id=${nextTask.id} --status=in-progress`)}\n` +
+			`${chalk.cyan('2.')} Mark as done when completed: ${chalk.yellow(`task-master set-status --id=${nextTask.id} --status=done`)}\n` +
+			`${chalk.cyan('3.')} View parent task: ${chalk.yellow(`task-master show --id=${nextTask.parentId}`)}`;
+	} else {
+		// Suggested actions for a parent task
+		suggestedActionsContent +=
+			`${chalk.cyan('1.')} Mark as in-progress: ${chalk.yellow(`task-master set-status --id=${nextTask.id} --status=in-progress`)}\n` +
+			`${chalk.cyan('2.')} Mark as done when completed: ${chalk.yellow(`task-master set-status --id=${nextTask.id} --status=done`)}\n` +
+			(nextTask.subtasks && nextTask.subtasks.length > 0
+				? `${chalk.cyan('3.')} Update subtask status: ${chalk.yellow(`task-master set-status --id=${nextTask.id}.1 --status=done`)}` // Example: first subtask
+				: `${chalk.cyan('3.')} Break down into subtasks: ${chalk.yellow(`task-master expand --id=${nextTask.id}`)}`);
+	}
+
 	console.log(
-		boxen(
+		boxen(suggestedActionsContent, {
-			chalk.white.bold('Suggested Actions:') +
+			padding: { top: 0, bottom: 0, left: 1, right: 1 },
-				'\n' +
+			borderColor: 'green',
-				`${chalk.cyan('1.')} Mark as in-progress: ${chalk.yellow(`task-master set-status --id=${nextTask.id} --status=in-progress`)}\n` +
+			borderStyle: 'round',
-				`${chalk.cyan('2.')} Mark as done when completed: ${chalk.yellow(`task-master set-status --id=${nextTask.id} --status=done`)}\n` +
+			margin: { top: 1 }
-				(nextTask.subtasks && nextTask.subtasks.length > 0
+		})
-					? `${chalk.cyan('3.')} Update subtask status: ${chalk.yellow(`task-master set-status --id=${nextTask.id}.1 --status=done`)}`
-					: `${chalk.cyan('3.')} Break down into subtasks: ${chalk.yellow(`task-master expand --id=${nextTask.id}`)}`),
-			{
-				padding: { top: 0, bottom: 0, left: 1, right: 1 },
-				borderColor: 'green',
-				borderStyle: 'round',
-				margin: { top: 1 }
-			}
-		)
 	);
 }
 
@@ -1011,7 +1029,12 @@ async function displayNextTask(tasksPath) {
  * @param {string|number} taskId - The ID of the task to display
  * @param {string} [statusFilter] - Optional status to filter subtasks by
  */
-async function displayTaskById(tasksPath, taskId, statusFilter = null) {
+async function displayTaskById(
+	tasksPath,
+	taskId,
+	complexityReportPath = null,
+	statusFilter = null
+) {
 	displayBanner();
 
 	// Read the tasks file
@@ -1021,11 +1044,15 @@ async function displayTaskById(tasksPath, taskId, statusFilter = null) {
 		process.exit(1);
 	}
 
+	// Read complexity report once
+	const complexityReport = readComplexityReport(complexityReportPath);
+
 	// Find the task by ID, applying the status filter if provided
 	// Returns { task, originalSubtaskCount, originalSubtasks }
 	const { task, originalSubtaskCount, originalSubtasks } = findTaskById(
 		data.tasks,
 		taskId,
+		complexityReport,
 		statusFilter
 	);
 
@@ -1080,6 +1107,12 @@ async function displayTaskById(tasksPath, taskId, statusFilter = null) {
 				chalk.cyan.bold('Status:'),
 				getStatusWithColor(task.status || 'pending', true)
 			],
+			[
+				chalk.cyan.bold('Complexity:'),
+				task.complexityScore
+					? getComplexityWithColor(task.complexityScore)
+					: chalk.gray('N/A')
+			],
 			[
 				chalk.cyan.bold('Description:'),
 				task.description || 'No description provided.'
@@ -1158,7 +1191,18 @@ async function displayTaskById(tasksPath, taskId, statusFilter = null) {
 		[chalk.cyan.bold('Priority:'), priorityColor(task.priority || 'medium')],
 		[
 			chalk.cyan.bold('Dependencies:'),
-			formatDependenciesWithStatus(task.dependencies, data.tasks, true)
+			formatDependenciesWithStatus(
+				task.dependencies,
+				data.tasks,
+				true,
+				complexityReport
+			)
+		],
+		[
+			chalk.cyan.bold('Complexity:'),
+			task.complexityScore
+				? getComplexityWithColor(task.complexityScore)
+				: chalk.gray('N/A')
 		],
 		[chalk.cyan.bold('Description:'), task.description]
 	);
@@ -1974,6 +2018,51 @@ function displayAvailableModels(availableModels) {
 	);
 }
 
+/**
+ * Displays AI usage telemetry summary in the CLI.
+ * @param {object} telemetryData - The telemetry data object.
+ * @param {string} outputType - 'cli' or 'mcp' (though typically only called for 'cli').
+ */
+function displayAiUsageSummary(telemetryData, outputType = 'cli') {
+	if (
+		(outputType !== 'cli' && outputType !== 'text') ||
+		!telemetryData ||
+		isSilentMode()
+	) {
+		return; // Only display for CLI and if data exists and not in silent mode
+	}
+
+	const {
+		modelUsed,
+		providerName,
+		inputTokens,
+		outputTokens,
+		totalTokens,
+		totalCost,
+		commandName
+	} = telemetryData;
+
+	let summary = chalk.bold.blue('AI Usage Summary:') + '\n';
+	summary += chalk.gray(`  Command: ${commandName}\n`);
+	summary += chalk.gray(`  Provider: ${providerName}\n`);
+	summary += chalk.gray(`  Model: ${modelUsed}\n`);
+	summary += chalk.gray(
+		`  Tokens: ${totalTokens} (Input: ${inputTokens}, Output: ${outputTokens})\n`
+	);
+	summary += chalk.gray(`  Est. Cost: $${totalCost.toFixed(6)}`);
+
+	console.log(
+		boxen(summary, {
+			padding: 1,
+			margin: { top: 1 },
+			borderColor: 'blue',
+			borderStyle: 'round',
+			title: '💡 Telemetry',
+			titleAlignment: 'center'
+		})
+	);
+}
+
 // Export UI functions
 export {
 	displayBanner,
@@ -1991,5 +2080,6 @@ export {
 	confirmTaskOverwrite,
 	displayApiKeyStatus,
 	displayModelConfiguration,
-	displayAvailableModels
+	displayAvailableModels,
+	displayAiUsageSummary
 };

scripts/modules/utils.js

@@ -275,6 +275,22 @@ function findTaskInComplexityReport(report, taskId) {
 	return report.complexityAnalysis.find((task) => task.taskId === taskId);
 }
 
+function addComplexityToTask(task, complexityReport) {
+	let taskId;
+	if (task.isSubtask) {
+		taskId = task.parentTask.id;
+	} else if (task.parentId) {
+		taskId = task.parentId;
+	} else {
+		taskId = task.id;
+	}
+
+	const taskAnalysis = findTaskInComplexityReport(complexityReport, taskId);
+	if (taskAnalysis) {
+		task.complexityScore = taskAnalysis.complexityScore;
+	}
+}
+
 /**
  * Checks if a task exists in the tasks array
  * @param {Array} tasks - The tasks array
@@ -325,10 +341,17 @@ function formatTaskId(id) {
  * Finds a task by ID in the tasks array. Optionally filters subtasks by status.
  * @param {Array} tasks - The tasks array
  * @param {string|number} taskId - The task ID to find
+ * @param {Object|null} complexityReport - Optional pre-loaded complexity report
+ * @returns {Object|null} The task object or null if not found
  * @param {string} [statusFilter] - Optional status to filter subtasks by
  * @returns {{task: Object|null, originalSubtaskCount: number|null}} The task object (potentially with filtered subtasks) and the original subtask count if filtered, or nulls if not found.
  */
-function findTaskById(tasks, taskId, statusFilter = null) {
+function findTaskById(
+	tasks,
+	taskId,
+	complexityReport = null,
+	statusFilter = null
+) {
 	if (!taskId || !tasks || !Array.isArray(tasks)) {
 		return { task: null, originalSubtaskCount: null };
 	}
@@ -356,10 +379,17 @@ function findTaskById(tasks, taskId, statusFilter = null) {
 			subtask.isSubtask = true;
 		}
 
-		// Return the found subtask (or null) and null for originalSubtaskCount
+		// If we found a task, check for complexity data
+		if (subtask && complexityReport) {
+			addComplexityToTask(subtask, complexityReport);
+		}
+
 		return { task: subtask || null, originalSubtaskCount: null };
 	}
 
+	let taskResult = null;
+	let originalSubtaskCount = null;
+
 	// Find the main task
 	const id = parseInt(taskId, 10);
 	const task = tasks.find((t) => t.id === id) || null;
@@ -369,6 +399,8 @@ function findTaskById(tasks, taskId, statusFilter = null) {
 		return { task: null, originalSubtaskCount: null };
 	}
 
+	taskResult = task;
+
 	// If task found and statusFilter provided, filter its subtasks
 	if (statusFilter && task.subtasks && Array.isArray(task.subtasks)) {
 		const originalSubtaskCount = task.subtasks.length;
@@ -379,12 +411,18 @@ function findTaskById(tasks, taskId, statusFilter = null) {
 				subtask.status &&
 				subtask.status.toLowerCase() === statusFilter.toLowerCase()
 		);
-		// Return the filtered task and the original count
+
-		return { task: filteredTask, originalSubtaskCount: originalSubtaskCount };
+		taskResult = filteredTask;
+		originalSubtaskCount = originalSubtaskCount;
 	}
 
-	// Return original task and null count if no filter or no subtasks
+	// If task found and complexityReport provided, add complexity data
-	return { task: task, originalSubtaskCount: null };
+	if (taskResult && complexityReport) {
+		addComplexityToTask(taskResult, complexityReport);
+	}
+
+	// Return the found task and original subtask count
+	return { task: taskResult, originalSubtaskCount };
 }
 
 /**
@@ -508,6 +546,61 @@ function detectCamelCaseFlags(args) {
 	return camelCaseFlags;
 }
 
+/**
+ * Aggregates an array of telemetry objects into a single summary object.
+ * @param {Array<Object>} telemetryArray - Array of telemetryData objects.
+ * @param {string} overallCommandName - The name for the aggregated command.
+ * @returns {Object|null} Aggregated telemetry object or null if input is empty.
+ */
+function aggregateTelemetry(telemetryArray, overallCommandName) {
+	if (!telemetryArray || telemetryArray.length === 0) {
+		return null;
+	}
+
+	const aggregated = {
+		timestamp: new Date().toISOString(), // Use current time for aggregation time
+		userId: telemetryArray[0].userId, // Assume userId is consistent
+		commandName: overallCommandName,
+		modelUsed: 'Multiple', // Default if models vary
+		providerName: 'Multiple', // Default if providers vary
+		inputTokens: 0,
+		outputTokens: 0,
+		totalTokens: 0,
+		totalCost: 0,
+		currency: telemetryArray[0].currency || 'USD' // Assume consistent currency or default
+	};
+
+	const uniqueModels = new Set();
+	const uniqueProviders = new Set();
+	const uniqueCurrencies = new Set();
+
+	telemetryArray.forEach((item) => {
+		aggregated.inputTokens += item.inputTokens || 0;
+		aggregated.outputTokens += item.outputTokens || 0;
+		aggregated.totalCost += item.totalCost || 0;
+		uniqueModels.add(item.modelUsed);
+		uniqueProviders.add(item.providerName);
+		uniqueCurrencies.add(item.currency || 'USD');
+	});
+
+	aggregated.totalTokens = aggregated.inputTokens + aggregated.outputTokens;
+	aggregated.totalCost = parseFloat(aggregated.totalCost.toFixed(6)); // Fix precision
+
+	if (uniqueModels.size === 1) {
+		aggregated.modelUsed = [...uniqueModels][0];
+	}
+	if (uniqueProviders.size === 1) {
+		aggregated.providerName = [...uniqueProviders][0];
+	}
+	if (uniqueCurrencies.size > 1) {
+		aggregated.currency = 'Multiple'; // Mark if currencies actually differ
+	} else if (uniqueCurrencies.size === 1) {
+		aggregated.currency = [...uniqueCurrencies][0];
+	}
+
+	return aggregated;
+}
+
 // Export all utility functions and configuration
 export {
 	LOG_LEVELS,
@@ -524,10 +617,12 @@ export {
 	findCycles,
 	toKebabCase,
 	detectCamelCaseFlags,
-	enableSilentMode,
 	disableSilentMode,
-	isSilentMode,
+	enableSilentMode,
-	resolveEnvVariable,
 	getTaskManager,
-	findProjectRoot
+	isSilentMode,
+	addComplexityToTask,
+	resolveEnvVariable,
+	findProjectRoot,
+	aggregateTelemetry
 };

scripts/task-complexity-report.json

@@ -1,7 +1,7 @@
 {
 	"meta": {
-		"generatedAt": "2025-05-03T04:45:36.864Z",
+		"generatedAt": "2025-05-17T22:29:22.179Z",
-		"tasksAnalyzed": 36,
+		"tasksAnalyzed": 40,
 		"thresholdScore": 5,
 		"projectName": "Taskmaster",
 		"usedResearch": false
@@ -10,290 +10,322 @@
 		{
 			"taskId": 24,
 			"taskTitle": "Implement AI-Powered Test Generation Command",
-			"complexityScore": 8,
+			"complexityScore": 7,
 			"recommendedSubtasks": 5,
-			"expansionPrompt": "Expand the 'Implement AI-Powered Test Generation Command' task by detailing the specific steps required for AI prompt engineering, including data extraction, prompt formatting, and error handling.",
+			"expansionPrompt": "Break down the implementation of the AI-powered test generation command into detailed subtasks covering: command structure setup, AI prompt engineering, test file generation logic, integration with Claude API, and comprehensive error handling.",
-			"reasoning": "Requires AI integration, complex logic, and thorough testing. Prompt engineering and API interaction add significant complexity."
+			"reasoning": "This task involves complex integration with an AI service (Claude), requires sophisticated prompt engineering, and needs to generate structured code files. The existing 3 subtasks are a good start but could be expanded to include more detailed steps for AI integration, error handling, and test file formatting."
 		},
 		{
 			"taskId": 26,
 			"taskTitle": "Implement Context Foundation for AI Operations",
-			"complexityScore": 7,
+			"complexityScore": 6,
-			"recommendedSubtasks": 6,
+			"recommendedSubtasks": 4,
-			"expansionPrompt": "Expand the 'Implement Context Foundation for AI Operations' task by detailing the specific steps for integrating file reading, cursor rules, and basic context extraction into the Claude API prompts.",
+			"expansionPrompt": "The current 4 subtasks for implementing the context foundation appear comprehensive. Consider if any additional subtasks are needed for testing, documentation, or integration with existing systems.",
-			"reasoning": "Involves modifying multiple commands and integrating different context sources. Error handling and backwards compatibility are crucial."
+			"reasoning": "This task involves creating a foundation for context integration with several well-defined components. The existing 4 subtasks cover the main implementation areas (context-file flag, cursor rules integration, context extraction utility, and command handler updates). The complexity is moderate as it requires careful integration with existing systems but has clear requirements."
 		},
 		{
 			"taskId": 27,
 			"taskTitle": "Implement Context Enhancements for AI Operations",
-			"complexityScore": 8,
+			"complexityScore": 7,
-			"recommendedSubtasks": 6,
+			"recommendedSubtasks": 4,
-			"expansionPrompt": "Expand the 'Implement Context Enhancements for AI Operations' task by detailing the specific steps for code context extraction, task history integration, and PRD context integration, including parsing, summarization, and formatting.",
+			"expansionPrompt": "The current 4 subtasks for implementing context enhancements appear well-structured. Consider if any additional subtasks are needed for testing, documentation, or performance optimization.",
-			"reasoning": "Builds upon the previous task with more sophisticated context extraction and integration. Requires intelligent parsing and summarization."
+			"reasoning": "This task builds upon the foundation from Task #26 and adds more sophisticated context handling features. The 4 existing subtasks cover the main implementation areas (code context extraction, task history context, PRD context integration, and context formatting). The complexity is higher than the foundation task due to the need for intelligent context selection and optimization."
 		},
 		{
 			"taskId": 28,
 			"taskTitle": "Implement Advanced ContextManager System",
-			"complexityScore": 9,
+			"complexityScore": 8,
-			"recommendedSubtasks": 7,
+			"recommendedSubtasks": 5,
-			"expansionPrompt": "Expand the 'Implement Advanced ContextManager System' task by detailing the specific steps for creating the ContextManager class, implementing the optimization pipeline, and adding command interface enhancements, including caching and performance monitoring.",
+			"expansionPrompt": "The current 5 subtasks for implementing the advanced ContextManager system appear comprehensive. Consider if any additional subtasks are needed for testing, documentation, or backward compatibility with previous context implementations.",
-			"reasoning": "A comprehensive system requiring careful design, optimization, and testing. Involves complex algorithms and performance considerations."
+			"reasoning": "This task represents the most complex phase of the context implementation, requiring a sophisticated class design, optimization algorithms, and integration with multiple systems. The 5 existing subtasks cover the core implementation areas, but the complexity is high due to the need for intelligent context prioritization, token management, and performance monitoring."
-		},
-		{
-			"taskId": 32,
-			"taskTitle": "Implement \"learn\" Command for Automatic Cursor Rule Generation",
-			"complexityScore": 9,
-			"recommendedSubtasks": 10,
-			"expansionPrompt": "Expand the 'Implement \"learn\" Command for Automatic Cursor Rule Generation' task by detailing the specific steps for Cursor data analysis, rule management, and AI integration, including error handling and performance optimization.",
-			"reasoning": "Requires deep integration with Cursor's data, complex pattern analysis, and AI interaction. Significant error handling and performance optimization are needed."
 		},
 		{
 			"taskId": 40,
 			"taskTitle": "Implement 'plan' Command for Task Implementation Planning",
-			"complexityScore": 6,
+			"complexityScore": 5,
 			"recommendedSubtasks": 4,
-			"expansionPrompt": "Expand the 'Implement 'plan' Command for Task Implementation Planning' task by detailing the steps for retrieving task content, generating implementation plans with AI, and formatting the plan within XML tags.",
+			"expansionPrompt": "The current 4 subtasks for implementing the 'plan' command appear well-structured. Consider if any additional subtasks are needed for testing, documentation, or integration with existing task management workflows.",
-			"reasoning": "Involves AI integration and requires careful formatting and error handling. Switching between Claude and Perplexity adds complexity."
+			"reasoning": "This task involves creating a new command that leverages AI to generate implementation plans. The existing 4 subtasks cover the main implementation areas (retrieving task content, generating plans with AI, formatting in XML, and error handling). The complexity is moderate as it builds on existing patterns for task updates but requires careful AI integration."
 		},
 		{
 			"taskId": 41,
 			"taskTitle": "Implement Visual Task Dependency Graph in Terminal",
 			"complexityScore": 8,
-			"recommendedSubtasks": 8,
+			"recommendedSubtasks": 10,
-			"expansionPrompt": "Expand the 'Implement Visual Task Dependency Graph in Terminal' task by detailing the steps for designing the graph rendering system, implementing layout algorithms, and handling circular dependencies and filtering options.",
+			"expansionPrompt": "The current 10 subtasks for implementing the visual task dependency graph appear comprehensive. Consider if any additional subtasks are needed for performance optimization with large graphs or additional visualization options.",
-			"reasoning": "Requires complex graph algorithms and terminal rendering. Accessibility and performance are important considerations."
+			"reasoning": "This task involves creating a sophisticated visualization system for terminal display, which is inherently complex due to layout algorithms, ASCII/Unicode rendering, and handling complex dependency relationships. The 10 existing subtasks cover all major aspects of implementation, from CLI interface to accessibility features."
 		},
 		{
 			"taskId": 42,
 			"taskTitle": "Implement MCP-to-MCP Communication Protocol",
-			"complexityScore": 8,
+			"complexityScore": 9,
-			"recommendedSubtasks": 7,
+			"recommendedSubtasks": 8,
-			"expansionPrompt": "Expand the 'Implement MCP-to-MCP Communication Protocol' task by detailing the steps for defining the protocol, implementing the adapter pattern, and building the client module, including error handling and security considerations.",
+			"expansionPrompt": "The current 8 subtasks for implementing the MCP-to-MCP communication protocol appear well-structured. Consider if any additional subtasks are needed for security hardening, performance optimization, or comprehensive documentation.",
-			"reasoning": "Requires designing a new protocol and implementing communication with external systems. Security and error handling are critical."
+			"reasoning": "This task involves designing and implementing a complex communication protocol between different MCP tools and servers. It requires sophisticated adapter patterns, client-server architecture, and handling of multiple operational modes. The complexity is very high due to the need for standardization, security, and backward compatibility."
-		},
-		{
-			"taskId": 43,
-			"taskTitle": "Add Research Flag to Add-Task Command",
-			"complexityScore": 5,
-			"recommendedSubtasks": 3,
-			"expansionPrompt": "Expand the 'Add Research Flag to Add-Task Command' task by detailing the steps for updating the command parser, generating research subtasks, and linking them to the parent task.",
-			"reasoning": "Relatively straightforward, but requires careful handling of subtask generation and linking."
 		},
 		{
 			"taskId": 44,
 			"taskTitle": "Implement Task Automation with Webhooks and Event Triggers",
 			"complexityScore": 8,
 			"recommendedSubtasks": 7,
-			"expansionPrompt": "Expand the 'Implement Task Automation with Webhooks and Event Triggers' task by detailing the steps for implementing the webhook registration system, event system, and trigger definition interface, including security and error handling.",
+			"expansionPrompt": "The current 7 subtasks for implementing task automation with webhooks appear comprehensive. Consider if any additional subtasks are needed for security testing, rate limiting implementation, or webhook monitoring tools.",
-			"reasoning": "Requires designing a robust event system and integrating with external services. Security and error handling are critical."
+			"reasoning": "This task involves creating a sophisticated event system with webhooks for integration with external services. The complexity is high due to the need for secure authentication, reliable delivery mechanisms, and handling of various webhook formats and protocols. The existing subtasks cover the main implementation areas but security and monitoring could be emphasized more."
 		},
 		{
 			"taskId": 45,
 			"taskTitle": "Implement GitHub Issue Import Feature",
-			"complexityScore": 7,
+			"complexityScore": 6,
 			"recommendedSubtasks": 5,
-			"expansionPrompt": "Expand the 'Implement GitHub Issue Import Feature' task by detailing the steps for parsing the URL, fetching issue details from the GitHub API, and generating a well-formatted task.",
+			"expansionPrompt": "The current 5 subtasks for implementing the GitHub issue import feature appear well-structured. Consider if any additional subtasks are needed for handling GitHub API rate limiting, caching, or supporting additional issue metadata.",
-			"reasoning": "Requires interacting with the GitHub API and handling various error conditions. Authentication adds complexity."
+			"reasoning": "This task involves integrating with the GitHub API to import issues as tasks. The complexity is moderate as it requires API authentication, data mapping, and error handling. The existing 5 subtasks cover the main implementation areas from design to end-to-end implementation."
 		},
 		{
 			"taskId": 46,
 			"taskTitle": "Implement ICE Analysis Command for Task Prioritization",
 			"complexityScore": 7,
 			"recommendedSubtasks": 5,
-			"expansionPrompt": "Expand the 'Implement ICE Analysis Command for Task Prioritization' task by detailing the steps for calculating ICE scores, generating the report file, and implementing the CLI rendering.",
+			"expansionPrompt": "The current 5 subtasks for implementing the ICE analysis command appear comprehensive. Consider if any additional subtasks are needed for visualization of ICE scores or integration with other prioritization methods.",
-			"reasoning": "Requires AI integration for scoring and careful formatting of the report. Integration with existing complexity reports adds complexity."
+			"reasoning": "This task involves creating an AI-powered analysis system for task prioritization using the ICE methodology. The complexity is high due to the need for sophisticated scoring algorithms, AI integration, and report generation. The existing subtasks cover the main implementation areas from algorithm design to integration with existing systems."
 		},
 		{
 			"taskId": 47,
 			"taskTitle": "Enhance Task Suggestion Actions Card Workflow",
-			"complexityScore": 7,
+			"complexityScore": 6,
 			"recommendedSubtasks": 6,
-			"expansionPrompt": "Expand the 'Enhance Task Suggestion Actions Card Workflow' task by detailing the steps for implementing the task expansion, context addition, and task management phases, including UI/UX considerations.",
+			"expansionPrompt": "The current 6 subtasks for enhancing the task suggestion actions card workflow appear well-structured. Consider if any additional subtasks are needed for user testing, accessibility improvements, or performance optimization.",
-			"reasoning": "Requires significant UI/UX work and careful state management. Integration with existing functionality is crucial."
+			"reasoning": "This task involves redesigning the UI workflow for task expansion and management. The complexity is moderate as it requires careful UX design and state management but builds on existing components. The 6 existing subtasks cover the main implementation areas from design to testing."
 		},
 		{
 			"taskId": 48,
 			"taskTitle": "Refactor Prompts into Centralized Structure",
-			"complexityScore": 5,
+			"complexityScore": 4,
 			"recommendedSubtasks": 3,
-			"expansionPrompt": "Expand the 'Refactor Prompts into Centralized Structure' task by detailing the steps for creating the 'prompts' directory, extracting prompts into individual files, and updating functions to import them.",
+			"expansionPrompt": "The current 3 subtasks for refactoring prompts into a centralized structure appear appropriate. Consider if any additional subtasks are needed for prompt versioning, documentation, or testing.",
-			"reasoning": "Primarily a refactoring task, but requires careful attention to detail to avoid breaking existing functionality."
+			"reasoning": "This task involves a straightforward refactoring to improve code organization. The complexity is relatively low as it primarily involves moving code rather than creating new functionality. The 3 existing subtasks cover the main implementation areas from directory structure to integration."
 		},
 		{
 			"taskId": 49,
 			"taskTitle": "Implement Code Quality Analysis Command",
 			"complexityScore": 8,
 			"recommendedSubtasks": 6,
-			"expansionPrompt": "Expand the 'Implement Code Quality Analysis Command' task by detailing the steps for pattern recognition, best practice verification, and improvement recommendations, including AI integration and task creation.",
+			"expansionPrompt": "The current 6 subtasks for implementing the code quality analysis command appear comprehensive. Consider if any additional subtasks are needed for performance optimization with large codebases or integration with existing code quality tools.",
-			"reasoning": "Requires complex code analysis and AI integration. Generating actionable recommendations adds complexity."
+			"reasoning": "This task involves creating a sophisticated code analysis system with pattern recognition, best practice verification, and AI-powered recommendations. The complexity is high due to the need for code parsing, complex analysis algorithms, and integration with AI services. The existing subtasks cover the main implementation areas from algorithm design to user interface."
 		},
 		{
 			"taskId": 50,
 			"taskTitle": "Implement Test Coverage Tracking System by Task",
 			"complexityScore": 9,
-			"recommendedSubtasks": 7,
+			"recommendedSubtasks": 5,
-			"expansionPrompt": "Expand the 'Implement Test Coverage Tracking System by Task' task by detailing the steps for creating the tests.json file structure, developing the coverage report parser, and implementing the CLI commands and AI-powered test generation system.",
+			"expansionPrompt": "The current 5 subtasks for implementing the test coverage tracking system appear well-structured. Consider if any additional subtasks are needed for integration with CI/CD systems, performance optimization, or visualization tools.",
-			"reasoning": "A comprehensive system requiring deep integration with testing tools and AI. Maintaining bidirectional relationships adds complexity."
+			"reasoning": "This task involves creating a complex system that maps test coverage to specific tasks and subtasks. The complexity is very high due to the need for sophisticated data structures, integration with coverage tools, and AI-powered test generation. The existing subtasks are comprehensive and cover the main implementation areas from data structure design to AI integration."
 		},
 		{
 			"taskId": 51,
 			"taskTitle": "Implement Perplexity Research Command",
-			"complexityScore": 7,
+			"complexityScore": 6,
 			"recommendedSubtasks": 5,
-			"expansionPrompt": "Expand the 'Implement Perplexity Research Command' task by detailing the steps for creating the Perplexity API client, implementing task context extraction, and building the CLI interface.",
+			"expansionPrompt": "The current 5 subtasks for implementing the Perplexity research command appear comprehensive. Consider if any additional subtasks are needed for caching optimization, result formatting, or integration with other research tools.",
-			"reasoning": "Requires API integration and careful formatting of the research results. Caching adds complexity."
+			"reasoning": "This task involves creating a new command that integrates with the Perplexity AI API for research. The complexity is moderate as it requires API integration, context extraction, and result formatting. The 5 existing subtasks cover the main implementation areas from API client to caching system."
 		},
 		{
 			"taskId": 52,
 			"taskTitle": "Implement Task Suggestion Command for CLI",
-			"complexityScore": 7,
+			"complexityScore": 6,
 			"recommendedSubtasks": 5,
-			"expansionPrompt": "Expand the 'Implement Task Suggestion Command for CLI' task by detailing the steps for collecting existing task data, generating task suggestions with AI, and implementing the interactive CLI interface.",
+			"expansionPrompt": "The current 5 subtasks for implementing the task suggestion command appear well-structured. Consider if any additional subtasks are needed for suggestion quality evaluation, user feedback collection, or integration with existing task workflows.",
-			"reasoning": "Requires AI integration and careful design of the interactive interface. Handling various flag combinations adds complexity."
+			"reasoning": "This task involves creating a new CLI command that generates contextually relevant task suggestions using AI. The complexity is moderate as it requires AI integration, context collection, and interactive CLI interfaces. The existing subtasks cover the main implementation areas from data collection to user interface."
 		},
 		{
 			"taskId": 53,
 			"taskTitle": "Implement Subtask Suggestion Feature for Parent Tasks",
-			"complexityScore": 7,
+			"complexityScore": 6,
 			"recommendedSubtasks": 6,
-			"expansionPrompt": "Expand the 'Implement Subtask Suggestion Feature for Parent Tasks' task by detailing the steps for validating parent tasks, gathering context, generating subtask suggestions with AI, and implementing the interactive CLI interface.",
+			"expansionPrompt": "The current 6 subtasks for implementing the subtask suggestion feature appear comprehensive. Consider if any additional subtasks are needed for suggestion quality metrics, user feedback collection, or performance optimization.",
-			"reasoning": "Requires AI integration and careful design of the interactive interface. Linking subtasks to parent tasks adds complexity."
+			"reasoning": "This task involves creating a feature that suggests contextually relevant subtasks for parent tasks. The complexity is moderate as it builds on existing task management systems but requires sophisticated AI integration and context analysis. The 6 existing subtasks cover the main implementation areas from validation to testing."
 		},
 		{
 			"taskId": 55,
 			"taskTitle": "Implement Positional Arguments Support for CLI Commands",
-			"complexityScore": 7,
+			"complexityScore": 5,
 			"recommendedSubtasks": 5,
-			"expansionPrompt": "Expand the 'Implement Positional Arguments Support for CLI Commands' task by detailing the steps for updating the argument parsing logic, defining the positional argument order, and handling edge cases.",
+			"expansionPrompt": "The current 5 subtasks for implementing positional arguments support appear well-structured. Consider if any additional subtasks are needed for backward compatibility testing, documentation updates, or user experience improvements.",
-			"reasoning": "Requires careful modification of the command parsing logic and ensuring backward compatibility. Handling edge cases adds complexity."
+			"reasoning": "This task involves modifying the command parsing logic to support positional arguments alongside the existing flag-based syntax. The complexity is moderate as it requires careful handling of different argument styles and edge cases. The 5 existing subtasks cover the main implementation areas from analysis to documentation."
 		},
 		{
 			"taskId": 57,
 			"taskTitle": "Enhance Task-Master CLI User Experience and Interface",
 			"complexityScore": 7,
 			"recommendedSubtasks": 6,
-			"expansionPrompt": "Expand the 'Enhance Task-Master CLI User Experience and Interface' task by detailing the steps for log management, visual enhancements, interactive elements, and output formatting.",
+			"expansionPrompt": "The current 6 subtasks for enhancing the CLI user experience appear comprehensive. Consider if any additional subtasks are needed for accessibility testing, internationalization, or performance optimization.",
-			"reasoning": "Requires significant UI/UX work and careful consideration of different terminal environments. Reducing verbose logging adds complexity."
+			"reasoning": "This task involves a significant overhaul of the CLI interface to improve user experience. The complexity is high due to the breadth of changes (logging, visual elements, interactive components, etc.) and the need for consistent design across all commands. The 6 existing subtasks cover the main implementation areas from log management to help systems."
 		},
 		{
 			"taskId": 60,
 			"taskTitle": "Implement Mentor System with Round-Table Discussion Feature",
 			"complexityScore": 8,
 			"recommendedSubtasks": 7,
-			"expansionPrompt": "Expand the 'Implement Mentor System with Round-Table Discussion Feature' task by detailing the steps for mentor management, round-table discussion implementation, and integration with the task system, including LLM integration.",
+			"expansionPrompt": "The current 7 subtasks for implementing the mentor system appear well-structured. Consider if any additional subtasks are needed for mentor personality consistency, discussion quality evaluation, or performance optimization with multiple mentors.",
-			"reasoning": "Requires complex AI simulation and careful formatting of the discussion output. Integrating with the task system adds complexity."
+			"reasoning": "This task involves creating a sophisticated mentor simulation system with round-table discussions. The complexity is high due to the need for personality simulation, complex LLM integration, and structured discussion management. The 7 existing subtasks cover the main implementation areas from architecture to testing."
-		},
-		{
-			"taskId": 61,
-			"taskTitle": "Implement Flexible AI Model Management",
-			"complexityScore": 9,
-			"recommendedSubtasks": 8,
-			"expansionPrompt": "Expand the 'Implement Flexible AI Model Management' task by detailing the steps for creating the configuration management module, implementing the CLI command parser, and integrating the Vercel AI SDK.",
-			"reasoning": "Requires deep integration with multiple AI models and careful management of API keys and configuration options. Vercel AI SDK integration adds complexity."
 		},
 		{
 			"taskId": 62,
 			"taskTitle": "Add --simple Flag to Update Commands for Direct Text Input",
-			"complexityScore": 5,
+			"complexityScore": 4,
-			"recommendedSubtasks": 4,
+			"recommendedSubtasks": 8,
-			"expansionPrompt": "Expand the 'Add --simple Flag to Update Commands for Direct Text Input' task by detailing the steps for updating the command parsers, implementing the conditional logic, and formatting the user input with a timestamp.",
+			"expansionPrompt": "The current 8 subtasks for implementing the --simple flag appear comprehensive. Consider if any additional subtasks are needed for user experience testing or documentation updates.",
-			"reasoning": "Relatively straightforward, but requires careful attention to formatting and ensuring consistency with AI-processed updates."
+			"reasoning": "This task involves adding a simple flag option to bypass AI processing for updates. The complexity is relatively low as it primarily involves modifying existing command handlers and adding a flag. The 8 existing subtasks are very detailed and cover all aspects of implementation from command parsing to testing."
 		},
 		{
 			"taskId": 63,
 			"taskTitle": "Add pnpm Support for the Taskmaster Package",
-			"complexityScore": 7,
+			"complexityScore": 5,
-			"recommendedSubtasks": 6,
+			"recommendedSubtasks": 8,
-			"expansionPrompt": "Expand the 'Add pnpm Support for the Taskmaster Package' task by detailing the steps for updating the documentation, ensuring package scripts compatibility, and testing the installation and operation with pnpm.",
+			"expansionPrompt": "The current 8 subtasks for adding pnpm support appear comprehensive. Consider if any additional subtasks are needed for CI/CD integration, performance comparison, or documentation updates.",
-			"reasoning": "Requires careful attention to detail to ensure compatibility with pnpm's execution model. Testing and documentation are crucial."
+			"reasoning": "This task involves ensuring the package works correctly with pnpm as an alternative package manager. The complexity is moderate as it requires careful testing of installation processes and scripts across different environments. The 8 existing subtasks cover all major aspects from documentation to binary verification."
 		},
 		{
 			"taskId": 64,
 			"taskTitle": "Add Yarn Support for Taskmaster Installation",
-			"complexityScore": 7,
+			"complexityScore": 5,
-			"recommendedSubtasks": 6,
+			"recommendedSubtasks": 9,
-			"expansionPrompt": "Expand the 'Add Yarn Support for Taskmaster Installation' task by detailing the steps for updating package.json, adding Yarn-specific configuration files, and testing the installation and operation with Yarn.",
+			"expansionPrompt": "The current 9 subtasks for adding Yarn support appear comprehensive. Consider if any additional subtasks are needed for performance testing, CI/CD integration, or compatibility with different Yarn versions.",
-			"reasoning": "Requires careful attention to detail to ensure compatibility with Yarn's execution model. Testing and documentation are crucial."
+			"reasoning": "This task involves ensuring the package works correctly with Yarn as an alternative package manager. The complexity is moderate as it requires careful testing of installation processes and scripts across different environments. The 9 existing subtasks are very detailed and cover all aspects from configuration to testing."
 		},
 		{
 			"taskId": 65,
 			"taskTitle": "Add Bun Support for Taskmaster Installation",
-			"complexityScore": 7,
+			"complexityScore": 6,
 			"recommendedSubtasks": 6,
-			"expansionPrompt": "Expand the 'Add Bun Support for Taskmaster Installation' task by detailing the steps for updating the installation scripts, testing the installation and operation with Bun, and updating the documentation.",
+			"expansionPrompt": "The current 6 subtasks for adding Bun support appear well-structured. Consider if any additional subtasks are needed for handling Bun-specific issues, performance testing, or documentation updates.",
-			"reasoning": "Requires careful attention to detail to ensure compatibility with Bun's execution model. Testing and documentation are crucial."
+			"reasoning": "This task involves adding support for the newer Bun package manager. The complexity is slightly higher than the other package manager tasks due to Bun's differences from Node.js and potential compatibility issues. The 6 existing subtasks cover the main implementation areas from research to documentation."
-		},
-		{
-			"taskId": 66,
-			"taskTitle": "Support Status Filtering in Show Command for Subtasks",
-			"complexityScore": 5,
-			"recommendedSubtasks": 4,
-			"expansionPrompt": "Expand the 'Support Status Filtering in Show Command for Subtasks' task by detailing the steps for updating the command parser, modifying the show command handler, and updating the help documentation.",
-			"reasoning": "Relatively straightforward, but requires careful handling of status validation and filtering."
 		},
 		{
 			"taskId": 67,
 			"taskTitle": "Add CLI JSON output and Cursor keybindings integration",
-			"complexityScore": 7,
+			"complexityScore": 5,
-			"recommendedSubtasks": 6,
+			"recommendedSubtasks": 5,
-			"expansionPrompt": "Expand the 'Add CLI JSON output and Cursor keybindings integration' task by detailing the steps for implementing the JSON output logic, creating the install-keybindings command structure, and handling keybinding file manipulation.",
+			"expansionPrompt": "The current 5 subtasks for implementing JSON output and Cursor keybindings appear well-structured. Consider if any additional subtasks are needed for testing across different operating systems, documentation updates, or user experience improvements.",
-			"reasoning": "Requires careful formatting of the JSON output and handling of file system operations. OS detection adds complexity."
+			"reasoning": "This task involves two distinct features: adding JSON output to CLI commands and creating a keybindings installation command. The complexity is moderate as it requires careful handling of different output formats and OS-specific file paths. The 5 existing subtasks cover the main implementation areas for both features."
 		},
 		{
 			"taskId": 68,
 			"taskTitle": "Ability to create tasks without parsing PRD",
 			"complexityScore": 3,
 			"recommendedSubtasks": 2,
-			"expansionPrompt": "Expand the 'Ability to create tasks without parsing PRD' task by detailing the steps for creating tasks without a PRD.",
+			"expansionPrompt": "The current 2 subtasks for implementing task creation without PRD appear appropriate. Consider if any additional subtasks are needed for validation, error handling, or integration with existing task management workflows.",
-			"reasoning": "Simple task to allow task creation without a PRD."
+			"reasoning": "This task involves a relatively simple modification to allow task creation without requiring a PRD document. The complexity is low as it primarily involves creating a form interface and saving functionality. The 2 existing subtasks cover the main implementation areas of UI design and data saving."
 		},
 		{
 			"taskId": 69,
 			"taskTitle": "Enhance Analyze Complexity for Specific Task IDs",
-			"complexityScore": 6,
+			"complexityScore": 5,
 			"recommendedSubtasks": 4,
-			"expansionPrompt": "Expand the 'Enhance Analyze Complexity for Specific Task IDs' task by detailing the steps for modifying the core logic, updating the CLI, and updating the MCP tool.",
+			"expansionPrompt": "The current 4 subtasks for enhancing the analyze-complexity feature appear well-structured. Consider if any additional subtasks are needed for performance optimization with large task sets or visualization improvements.",
-			"reasoning": "Requires modifying existing functionality and ensuring compatibility with both CLI and MCP."
+			"reasoning": "This task involves modifying the existing analyze-complexity feature to support analyzing specific task IDs and updating reports. The complexity is moderate as it requires careful handling of report merging and filtering logic. The 4 existing subtasks cover the main implementation areas from core logic to testing."
 		},
 		{
 			"taskId": 70,
 			"taskTitle": "Implement 'diagram' command for Mermaid diagram generation",
 			"complexityScore": 6,
 			"recommendedSubtasks": 4,
-			"expansionPrompt": "Expand the 'Implement 'diagram' command for Mermaid diagram generation' task by detailing the steps for creating the command, generating the Mermaid diagram, and handling different output options.",
+			"expansionPrompt": "The current 4 subtasks for implementing the 'diagram' command appear well-structured. Consider if any additional subtasks are needed for handling large dependency graphs, additional output formats, or integration with existing visualization tools.",
-			"reasoning": "Requires generating Mermaid diagrams and handling different output options."
+			"reasoning": "This task involves creating a new command that generates Mermaid diagrams to visualize task dependencies. The complexity is moderate as it requires parsing task relationships, generating proper Mermaid syntax, and handling various output options. The 4 existing subtasks cover the main implementation areas from interface design to documentation."
 		},
 		{
 			"taskId": 72,
 			"taskTitle": "Implement PDF Generation for Project Progress and Dependency Overview",
-			"complexityScore": 8,
-			"recommendedSubtasks": 6,
-			"expansionPrompt": "Expand the 'Implement PDF Generation for Project Progress and Dependency Overview' task by detailing the steps for summarizing project progress, visualizing the dependency chain, and generating the PDF document.",
-			"reasoning": "Requires integrating with the diagram command and using a PDF generation library. Handling large dependency chains adds complexity."
-		},
-		{
-			"taskId": 73,
-			"taskTitle": "Implement Custom Model ID Support for Ollama/OpenRouter",
 			"complexityScore": 7,
-			"recommendedSubtasks": 5,
+			"recommendedSubtasks": 6,
-			"expansionPrompt": "Expand the 'Implement Custom Model ID Support for Ollama/OpenRouter' task by detailing the steps for modifying the CLI, implementing the interactive setup, and handling validation and warnings.",
+			"expansionPrompt": "The current 6 subtasks for implementing PDF generation appear comprehensive. Consider if any additional subtasks are needed for handling large projects, additional visualization options, or integration with existing reporting tools.",
-			"reasoning": "Requires integrating with external APIs and handling different model types. Validation and warnings are crucial."
+			"reasoning": "This task involves creating a feature to generate PDF reports of project progress and dependency visualization. The complexity is high due to the need for PDF generation, data collection, and visualization integration. The 6 existing subtasks cover the main implementation areas from library selection to export options."
 		},
 		{
 			"taskId": 75,
 			"taskTitle": "Integrate Google Search Grounding for Research Role",
-			"complexityScore": 6,
+			"complexityScore": 5,
 			"recommendedSubtasks": 4,
-			"expansionPrompt": "Expand the 'Integrate Google Search Grounding for Research Role' task by detailing the steps for modifying the AI service layer, implementing the conditional logic, and updating the supported models.",
+			"expansionPrompt": "The current 4 subtasks for integrating Google Search Grounding appear well-structured. Consider if any additional subtasks are needed for testing with different query types, error handling, or performance optimization.",
-			"reasoning": "Requires conditional logic and integration with the Google Search Grounding API."
+			"reasoning": "This task involves updating the AI service layer to enable Google Search Grounding for research roles. The complexity is moderate as it requires careful integration with the existing AI service architecture and conditional logic. The 4 existing subtasks cover the main implementation areas from service layer modification to testing."
 		},
 		{
 			"taskId": 76,
 			"taskTitle": "Develop E2E Test Framework for Taskmaster MCP Server (FastMCP over stdio)",
-			"complexityScore": 9,
+			"complexityScore": 8,
 			"recommendedSubtasks": 7,
-			"expansionPrompt": "Expand the 'Develop E2E Test Framework for Taskmaster MCP Server (FastMCP over stdio)' task by detailing the steps for launching the FastMCP server, implementing the message protocol handler, and developing the request/response correlation mechanism.",
+			"expansionPrompt": "The current 7 subtasks for developing the E2E test framework appear comprehensive. Consider if any additional subtasks are needed for test result reporting, CI/CD integration, or performance benchmarking.",
-			"reasoning": "Requires complex system integration and robust error handling. Designing a comprehensive test framework adds complexity."
+			"reasoning": "This task involves creating a sophisticated end-to-end testing framework for the MCP server. The complexity is high due to the need for subprocess management, protocol handling, and robust test case definition. The 7 existing subtasks cover the main implementation areas from architecture to documentation."
+		},
+		{
+			"taskId": 77,
+			"taskTitle": "Implement AI Usage Telemetry for Taskmaster (with external analytics endpoint)",
+			"complexityScore": 7,
+			"recommendedSubtasks": 18,
+			"expansionPrompt": "The current 18 subtasks for implementing AI usage telemetry appear very comprehensive. Consider if any additional subtasks are needed for security hardening, privacy compliance, or user feedback collection.",
+			"reasoning": "This task involves creating a telemetry system to track AI usage metrics. The complexity is high due to the need for secure data transmission, comprehensive data collection, and integration across multiple commands. The 18 existing subtasks are extremely detailed and cover all aspects of implementation from core utility to provider-specific updates."
+		},
+		{
+			"taskId": 80,
+			"taskTitle": "Implement Unique User ID Generation and Storage During Installation",
+			"complexityScore": 4,
+			"recommendedSubtasks": 5,
+			"expansionPrompt": "The current 5 subtasks for implementing unique user ID generation appear well-structured. Consider if any additional subtasks are needed for privacy compliance, security auditing, or integration with the telemetry system.",
+			"reasoning": "This task involves generating and storing a unique user identifier during installation. The complexity is relatively low as it primarily involves UUID generation and configuration file management. The 5 existing subtasks cover the main implementation areas from script structure to documentation."
+		},
+		{
+			"taskId": 81,
+			"taskTitle": "Task #81: Implement Comprehensive Local Telemetry System with Future Server Integration Capability",
+			"complexityScore": 8,
+			"recommendedSubtasks": 6,
+			"expansionPrompt": "The current 6 subtasks for implementing the comprehensive local telemetry system appear well-structured. Consider if any additional subtasks are needed for data migration, storage optimization, or visualization tools.",
+			"reasoning": "This task involves expanding the telemetry system to capture additional metrics and implement local storage with future server integration capability. The complexity is high due to the breadth of data collection, storage requirements, and privacy considerations. The 6 existing subtasks cover the main implementation areas from data collection to user-facing benefits."
+		},
+		{
+			"taskId": 82,
+			"taskTitle": "Update supported-models.json with token limit fields",
+			"complexityScore": 3,
+			"recommendedSubtasks": 1,
+			"expansionPrompt": "This task appears straightforward enough to be implemented without further subtasks. Focus on researching accurate token limit values for each model and ensuring backward compatibility.",
+			"reasoning": "This task involves a simple update to the supported-models.json file to include new token limit fields. The complexity is low as it primarily involves research and data entry. No subtasks are necessary as the task is well-defined and focused."
+		},
+		{
+			"taskId": 83,
+			"taskTitle": "Update config-manager.js defaults and getters",
+			"complexityScore": 4,
+			"recommendedSubtasks": 1,
+			"expansionPrompt": "This task appears straightforward enough to be implemented without further subtasks. Focus on updating the DEFAULTS object and related getter functions while maintaining backward compatibility.",
+			"reasoning": "This task involves updating the config-manager.js module to replace maxTokens with more specific token limit fields. The complexity is relatively low as it primarily involves modifying existing code rather than creating new functionality. No subtasks are necessary as the task is well-defined and focused."
+		},
+		{
+			"taskId": 84,
+			"taskTitle": "Implement token counting utility",
+			"complexityScore": 5,
+			"recommendedSubtasks": 1,
+			"expansionPrompt": "This task appears well-defined enough to be implemented without further subtasks. Focus on implementing accurate token counting for different models and proper fallback mechanisms.",
+			"reasoning": "This task involves creating a utility function to count tokens for different AI models. The complexity is moderate as it requires integration with the tiktoken library and handling different tokenization schemes. No subtasks are necessary as the task is well-defined and focused."
+		},
+		{
+			"taskId": 85,
+			"taskTitle": "Update ai-services-unified.js for dynamic token limits",
+			"complexityScore": 6,
+			"recommendedSubtasks": 1,
+			"expansionPrompt": "This task appears well-defined enough to be implemented without further subtasks. Focus on implementing dynamic token limit adjustment based on input length and model capabilities.",
+			"reasoning": "This task involves modifying the AI service runner to use the new token counting utility and dynamically adjust output token limits. The complexity is moderate to high as it requires careful integration with existing code and handling various edge cases. No subtasks are necessary as the task is well-defined and focused."
+		},
+		{
+			"taskId": 86,
+			"taskTitle": "Update .taskmasterconfig schema and user guide",
+			"complexityScore": 4,
+			"recommendedSubtasks": 1,
+			"expansionPrompt": "This task appears straightforward enough to be implemented without further subtasks. Focus on creating clear migration guidance and updating documentation.",
+			"reasoning": "This task involves creating a migration guide for users to update their configuration files and documenting the new token limit options. The complexity is relatively low as it primarily involves documentation and schema validation. No subtasks are necessary as the task is well-defined and focused."
+		},
+		{
+			"taskId": 87,
+			"taskTitle": "Implement validation and error handling",
+			"complexityScore": 5,
+			"recommendedSubtasks": 1,
+			"expansionPrompt": "This task appears well-defined enough to be implemented without further subtasks. Focus on comprehensive validation and helpful error messages throughout the system.",
+			"reasoning": "This task involves adding validation and error handling for token limits throughout the system. The complexity is moderate as it requires careful integration with multiple components and creating helpful error messages. No subtasks are necessary as the task is well-defined and focused."
 		}
 	]
 }

src/ai-providers/anthropic.js

@@ -5,7 +5,7 @@
  * using the Vercel AI SDK.
  */
 import { createAnthropic } from '@ai-sdk/anthropic';
-import { generateText, streamText, generateObject, streamObject } from 'ai';
+import { generateText, streamText, generateObject } from 'ai';
 import { log } from '../../scripts/modules/utils.js'; // Assuming utils is accessible
 
 // TODO: Implement standardized functions for generateText, streamText, generateObject
@@ -17,7 +17,7 @@ import { log } from '../../scripts/modules/utils.js'; // Assuming utils is acces
 // Remove the global variable and caching logic
 // let anthropicClient;
 
-function getClient(apiKey) {
+function getClient(apiKey, baseUrl) {
 	if (!apiKey) {
 		// In a real scenario, this would use the config resolver.
 		// Throwing error here if key isn't passed for simplicity.
@@ -30,14 +30,12 @@ function getClient(apiKey) {
 	// Create and return a new instance directly with standard version header
 	return createAnthropic({
 		apiKey: apiKey,
-		baseURL: 'https://api.anthropic.com/v1',
+		...(baseUrl && { baseURL: baseUrl }),
 		// Use standard version header instead of beta
 		headers: {
 			'anthropic-beta': 'output-128k-2025-02-19'
 		}
 	});
-	// }
-	// return anthropicClient;
 }
 
 // --- Standardized Service Function Implementations ---
@@ -51,7 +49,8 @@ function getClient(apiKey) {
  * @param {Array<object>} params.messages - The messages array (e.g., [{ role: 'user', content: '...' }]).
  * @param {number} [params.maxTokens] - Maximum tokens for the response.
  * @param {number} [params.temperature] - Temperature for generation.
- * @returns {Promise<string>} The generated text content.
+ * @param {string} [params.baseUrl] - The base URL for the Anthropic API.
+ * @returns {Promise<object>} The generated text content and usage.
  * @throws {Error} If the API call fails.
  */
 export async function generateAnthropicText({
@@ -59,11 +58,12 @@ export async function generateAnthropicText({
 	modelId,
 	messages,
 	maxTokens,
-	temperature
+	temperature,
+	baseUrl
 }) {
 	log('debug', `Generating Anthropic text with model: ${modelId}`);
 	try {
-		const client = getClient(apiKey);
+		const client = getClient(apiKey, baseUrl);
 		const result = await generateText({
 			model: client(modelId),
 			messages: messages,
@@ -76,7 +76,14 @@ export async function generateAnthropicText({
 			'debug',
 			`Anthropic generateText result received. Tokens: ${result.usage.completionTokens}/${result.usage.promptTokens}`
 		);
-		return result.text;
+		// Return both text and usage
+		return {
+			text: result.text,
+			usage: {
+				inputTokens: result.usage.promptTokens,
+				outputTokens: result.usage.completionTokens
+			}
+		};
 	} catch (error) {
 		log('error', `Anthropic generateText failed: ${error.message}`);
 		// Consider more specific error handling or re-throwing a standardized error
@@ -93,6 +100,7 @@ export async function generateAnthropicText({
  * @param {Array<object>} params.messages - The messages array.
  * @param {number} [params.maxTokens] - Maximum tokens for the response.
  * @param {number} [params.temperature] - Temperature for generation.
+ * @param {string} [params.baseUrl] - The base URL for the Anthropic API.
  * @returns {Promise<object>} The full stream result object from the Vercel AI SDK.
  * @throws {Error} If the API call fails to initiate the stream.
  */
@@ -101,20 +109,20 @@ export async function streamAnthropicText({
 	modelId,
 	messages,
 	maxTokens,
-	temperature
+	temperature,
+	baseUrl
 }) {
 	log('debug', `Streaming Anthropic text with model: ${modelId}`);
 	try {
-		const client = getClient(apiKey);
+		const client = getClient(apiKey, baseUrl);
 
-		// --- DEBUG LOGGING --- >>
 		log(
 			'debug',
 			'[streamAnthropicText] Parameters received by streamText:',
 			JSON.stringify(
 				{
-					modelId: modelId, // Log modelId being used
+					modelId: modelId,
-					messages: messages, // Log the messages array
+					messages: messages,
 					maxTokens: maxTokens,
 					temperature: temperature
 				},
@@ -122,25 +130,19 @@ export async function streamAnthropicText({
 				2
 			)
 		);
-		// --- << DEBUG LOGGING ---
 
 		const stream = await streamText({
 			model: client(modelId),
 			messages: messages,
 			maxTokens: maxTokens,
 			temperature: temperature
-			// Beta header moved to client initialization
 			// TODO: Add other relevant parameters
 		});
 
 		// *** RETURN THE FULL STREAM OBJECT, NOT JUST stream.textStream ***
 		return stream;
 	} catch (error) {
-		log(
+		log('error', `Anthropic streamText failed: ${error.message}`, error.stack);
-			'error',
-			`Anthropic streamText failed: ${error.message}`,
-			error.stack // Log stack trace for more details
-		);
 		throw error;
 	}
 }
@@ -160,7 +162,8 @@ export async function streamAnthropicText({
  * @param {number} [params.maxTokens] - Maximum tokens for the response.
  * @param {number} [params.temperature] - Temperature for generation.
  * @param {number} [params.maxRetries] - Max retries for validation/generation.
- * @returns {Promise<object>} The generated object matching the schema.
+ * @param {string} [params.baseUrl] - The base URL for the Anthropic API.
+ * @returns {Promise<object>} The generated object matching the schema and usage.
  * @throws {Error} If generation or validation fails.
  */
 export async function generateAnthropicObject({
@@ -171,24 +174,22 @@ export async function generateAnthropicObject({
 	objectName = 'generated_object',
 	maxTokens,
 	temperature,
-	maxRetries = 3
+	maxRetries = 3,
+	baseUrl
 }) {
 	log(
 		'debug',
 		`Generating Anthropic object ('${objectName}') with model: ${modelId}`
 	);
 	try {
-		const client = getClient(apiKey);
+		const client = getClient(apiKey, baseUrl);
-
-		// Log basic debug info
 		log(
 			'debug',
 			`Using maxTokens: ${maxTokens}, temperature: ${temperature}, model: ${modelId}`
 		);
-
 		const result = await generateObject({
 			model: client(modelId),
-			mode: 'tool', // Anthropic generally uses 'tool' mode for structured output
+			mode: 'tool',
 			schema: schema,
 			messages: messages,
 			tool: {
@@ -199,14 +200,19 @@ export async function generateAnthropicObject({
 			temperature: temperature,
 			maxRetries: maxRetries
 		});
-
 		log(
 			'debug',
 			`Anthropic generateObject result received. Tokens: ${result.usage.completionTokens}/${result.usage.promptTokens}`
 		);
-		return result.object;
+		// Return both object and usage
+		return {
+			object: result.object,
+			usage: {
+				inputTokens: result.usage.promptTokens,
+				outputTokens: result.usage.completionTokens
+			}
+		};
 	} catch (error) {
-		// Simple error logging
 		log(
 			'error',
 			`Anthropic generateObject ('${objectName}') failed: ${error.message}`

src/ai-providers/google.js

@@ -9,9 +9,19 @@ import { generateText, streamText, generateObject } from 'ai'; // Import from ma
 import { log } from '../../scripts/modules/utils.js'; // Import logging utility
 
 // Consider making model configurable via config-manager.js later
-const DEFAULT_MODEL = 'gemini-2.0-pro'; // Or a suitable default
+const DEFAULT_MODEL = 'gemini-2.5-pro-exp-03-25'; // Or a suitable default
 const DEFAULT_TEMPERATURE = 0.2; // Or a suitable default
 
+function getClient(apiKey, baseUrl) {
+	if (!apiKey) {
+		throw new Error('Google API key is required.');
+	}
+	return createGoogleGenerativeAI({
+		apiKey: apiKey,
+		...(baseUrl && { baseURL: baseUrl })
+	});
+}
+
 /**
  * Generates text using a Google AI model.
  *
@@ -29,7 +39,8 @@ async function generateGoogleText({
 	modelId = DEFAULT_MODEL,
 	temperature = DEFAULT_TEMPERATURE,
 	messages,
-	maxTokens // Note: Vercel SDK might handle this differently, needs verification
+	maxTokens,
+	baseUrl
 }) {
 	if (!apiKey) {
 		throw new Error('Google API key is required.');
@@ -37,28 +48,31 @@ async function generateGoogleText({
 	log('info', `Generating text with Google model: ${modelId}`);
 
 	try {
-		// const google = new GoogleGenerativeAI({ apiKey }); // Incorrect instantiation
+		const googleProvider = getClient(apiKey, baseUrl);
-		const googleProvider = createGoogleGenerativeAI({ apiKey }); // Correct instantiation
+		const model = googleProvider(modelId);
-		// const model = google.getGenerativeModel({ model: modelId }); // Incorrect model retrieval
-		const model = googleProvider(modelId); // Correct model retrieval
-
-		// Construct payload suitable for Vercel SDK's generateText
-		// Note: The exact structure might depend on how messages are passed
 		const result = await generateText({
-			model, // Pass the model instance
+			model,
-			messages, // Pass the messages array directly
+			messages,
 			temperature,
-			maxOutputTokens: maxTokens // Map to correct Vercel SDK param if available
+			maxOutputTokens: maxTokens
 		});
 
 		// Assuming result structure provides text directly or within a property
-		return result.text; // Adjust based on actual SDK response
+		// return result.text; // Adjust based on actual SDK response
+		// Return both text and usage
+		return {
+			text: result.text,
+			usage: {
+				inputTokens: result.usage.promptTokens,
+				outputTokens: result.usage.completionTokens
+			}
+		};
 	} catch (error) {
 		log(
 			'error',
 			`Error generating text with Google (${modelId}): ${error.message}`
 		);
-		throw error; // Re-throw for unified service handler
+		throw error;
 	}
 }
 
@@ -79,7 +93,8 @@ async function streamGoogleText({
 	modelId = DEFAULT_MODEL,
 	temperature = DEFAULT_TEMPERATURE,
 	messages,
-	maxTokens
+	maxTokens,
+	baseUrl
 }) {
 	if (!apiKey) {
 		throw new Error('Google API key is required.');
@@ -87,19 +102,15 @@ async function streamGoogleText({
 	log('info', `Streaming text with Google model: ${modelId}`);
 
 	try {
-		// const google = new GoogleGenerativeAI({ apiKey }); // Incorrect instantiation
+		const googleProvider = getClient(apiKey, baseUrl);
-		const googleProvider = createGoogleGenerativeAI({ apiKey }); // Correct instantiation
+		const model = googleProvider(modelId);
-		// const model = google.getGenerativeModel({ model: modelId }); // Incorrect model retrieval
-		const model = googleProvider(modelId); // Correct model retrieval
-
 		const stream = await streamText({
-			model, // Pass the model instance
+			model,
 			messages,
 			temperature,
 			maxOutputTokens: maxTokens
 		});
-
+		return stream;
-		return stream; // Return the stream directly
 	} catch (error) {
 		log(
 			'error',
@@ -130,7 +141,8 @@ async function generateGoogleObject({
 	messages,
 	schema,
 	objectName, // Note: Vercel SDK might use this differently or not at all
-	maxTokens
+	maxTokens,
+	baseUrl
 }) {
 	if (!apiKey) {
 		throw new Error('Google API key is required.');
@@ -138,23 +150,25 @@ async function generateGoogleObject({
 	log('info', `Generating object with Google model: ${modelId}`);
 
 	try {
-		// const google = new GoogleGenerativeAI({ apiKey }); // Incorrect instantiation
+		const googleProvider = getClient(apiKey, baseUrl);
-		const googleProvider = createGoogleGenerativeAI({ apiKey }); // Correct instantiation
+		const model = googleProvider(modelId);
-		// const model = google.getGenerativeModel({ model: modelId }); // Incorrect model retrieval
+		const result = await generateObject({
-		const model = googleProvider(modelId); // Correct model retrieval
+			model,
-
-		const { object } = await generateObject({
-			model, // Pass the model instance
 			schema,
 			messages,
 			temperature,
 			maxOutputTokens: maxTokens
-			// Note: 'objectName' or 'mode' might not be directly applicable here
-			// depending on how `@ai-sdk/google` handles `generateObject`.
-			// Check SDK docs if specific tool calling/JSON mode needs explicit setup.
 		});
 
-		return object; // Return the parsed object
+		// return object; // Return the parsed object
+		// Return both object and usage
+		return {
+			object: result.object,
+			usage: {
+				inputTokens: result.usage.promptTokens,
+				outputTokens: result.usage.completionTokens
+			}
+		};
 	} catch (error) {
 		log(
 			'error',

src/ai-providers/openai.js

@@ -1,16 +1,26 @@
-import { createOpenAI, openai } from '@ai-sdk/openai'; // Using openai provider from Vercel AI SDK
+import { createOpenAI } from '@ai-sdk/openai'; // Using openai provider from Vercel AI SDK
-import { generateText, streamText, generateObject } from 'ai'; // Import necessary functions from 'ai'
+import { generateObject, generateText } from 'ai'; // Import necessary functions from 'ai'
 import { log } from '../../scripts/modules/utils.js';
 
+function getClient(apiKey, baseUrl) {
+	if (!apiKey) {
+		throw new Error('OpenAI API key is required.');
+	}
+	return createOpenAI({
+		apiKey: apiKey,
+		...(baseUrl && { baseURL: baseUrl })
+	});
+}
+
 /**
  * Generates text using OpenAI models via Vercel AI SDK.
  *
- * @param {object} params - Parameters including apiKey, modelId, messages, maxTokens, temperature.
+ * @param {object} params - Parameters including apiKey, modelId, messages, maxTokens, temperature, baseUrl.
- * @returns {Promise<string>} The generated text content.
+ * @returns {Promise<object>} The generated text content and usage.
  * @throws {Error} If API call fails.
  */
 export async function generateOpenAIText(params) {
-	const { apiKey, modelId, messages, maxTokens, temperature } = params;
+	const { apiKey, modelId, messages, maxTokens, temperature, baseUrl } = params;
 	log('debug', `generateOpenAIText called with model: ${modelId}`);
 
 	if (!apiKey) {
@@ -23,21 +33,17 @@ export async function generateOpenAIText(params) {
 		throw new Error('Invalid or empty messages array provided for OpenAI.');
 	}
 
-	const openaiClient = createOpenAI({ apiKey });
+	const openaiClient = getClient(apiKey, baseUrl);
 
 	try {
-		const result = await openaiClient.chat(messages, {
+		const result = await generateText({
-			// Updated: Use openaiClient.chat directly
+			model: openaiClient(modelId),
-			model: modelId,
+			messages,
-			max_tokens: maxTokens,
+			maxTokens,
 			temperature
 		});
 
-		// Adjust based on actual Vercel SDK response structure for openaiClient.chat
+		if (!result || !result.text) {
-		// This might need refinement based on testing the SDK's output.
-		const textContent = result?.choices?.[0]?.message?.content?.trim();
-
-		if (!textContent) {
 			log(
 				'warn',
 				'OpenAI generateText response did not contain expected content.',
@@ -49,7 +55,13 @@ export async function generateOpenAIText(params) {
 			'debug',
 			`OpenAI generateText completed successfully for model: ${modelId}`
 		);
-		return textContent;
+		return {
+			text: result.text.trim(),
+			usage: {
+				inputTokens: result.usage.promptTokens,
+				outputTokens: result.usage.completionTokens
+			}
+		};
 	} catch (error) {
 		log(
 			'error',
@@ -65,12 +77,12 @@ export async function generateOpenAIText(params) {
 /**
  * Streams text using OpenAI models via Vercel AI SDK.
  *
- * @param {object} params - Parameters including apiKey, modelId, messages, maxTokens, temperature.
+ * @param {object} params - Parameters including apiKey, modelId, messages, maxTokens, temperature, baseUrl.
  * @returns {Promise<ReadableStream>} A readable stream of text deltas.
  * @throws {Error} If API call fails.
  */
 export async function streamOpenAIText(params) {
-	const { apiKey, modelId, messages, maxTokens, temperature } = params;
+	const { apiKey, modelId, messages, maxTokens, temperature, baseUrl } = params;
 	log('debug', `streamOpenAIText called with model: ${modelId}`);
 
 	if (!apiKey) {
@@ -85,12 +97,10 @@ export async function streamOpenAIText(params) {
 		);
 	}
 
-	const openaiClient = createOpenAI({ apiKey });
+	const openaiClient = getClient(apiKey, baseUrl);
 
 	try {
-		// Use the streamText function from Vercel AI SDK core
 		const stream = await openaiClient.chat.stream(messages, {
-			// Updated: Use openaiClient.chat.stream
 			model: modelId,
 			max_tokens: maxTokens,
 			temperature
@@ -100,7 +110,6 @@ export async function streamOpenAIText(params) {
 			'debug',
 			`OpenAI streamText initiated successfully for model: ${modelId}`
 		);
-		// The Vercel SDK's streamText should directly return the stream object
 		return stream;
 	} catch (error) {
 		log(
@@ -117,8 +126,8 @@ export async function streamOpenAIText(params) {
 /**
  * Generates structured objects using OpenAI models via Vercel AI SDK.
  *
- * @param {object} params - Parameters including apiKey, modelId, messages, schema, objectName, maxTokens, temperature.
+ * @param {object} params - Parameters including apiKey, modelId, messages, schema, objectName, maxTokens, temperature, baseUrl.
- * @returns {Promise<object>} The generated object matching the schema.
+ * @returns {Promise<object>} The generated object matching the schema and usage.
  * @throws {Error} If API call fails or object generation fails.
  */
 export async function generateOpenAIObject(params) {
@@ -129,7 +138,8 @@ export async function generateOpenAIObject(params) {
 		schema,
 		objectName,
 		maxTokens,
-		temperature
+		temperature,
+		baseUrl
 	} = params;
 	log(
 		'debug',
@@ -145,10 +155,9 @@ export async function generateOpenAIObject(params) {
 	if (!objectName)
 		throw new Error('Object name is required for OpenAI object generation.');
 
-	const openaiClient = createOpenAI({ apiKey });
+	const openaiClient = getClient(apiKey, baseUrl);
 
 	try {
-		// Use the imported generateObject function from 'ai' package
 		const result = await generateObject({
 			model: openaiClient(modelId),
 			schema: schema,
@@ -162,7 +171,21 @@ export async function generateOpenAIObject(params) {
 			'debug',
 			`OpenAI generateObject completed successfully for model: ${modelId}`
 		);
-		return result.object;
+		if (!result || typeof result.object === 'undefined') {
+			log(
+				'warn',
+				'OpenAI generateObject response did not contain expected object.',
+				{ result }
+			);
+			throw new Error('Failed to extract object from OpenAI response.');
+		}
+		return {
+			object: result.object,
+			usage: {
+				inputTokens: result.usage.promptTokens,
+				outputTokens: result.usage.completionTokens
+			}
+		};
 	} catch (error) {
 		log(
 			'error',

src/ai-providers/openrouter.js

@@ -2,6 +2,14 @@ import { createOpenRouter } from '@openrouter/ai-sdk-provider';
 import { generateText, streamText, generateObject } from 'ai';
 import { log } from '../../scripts/modules/utils.js'; // Assuming utils.js is in scripts/modules
 
+function getClient(apiKey, baseUrl) {
+	if (!apiKey) throw new Error('OpenRouter API key is required.');
+	return createOpenRouter({
+		apiKey,
+		...(baseUrl && { baseURL: baseUrl })
+	});
+}
+
 /**
  * Generates text using an OpenRouter chat model.
  *
@@ -11,6 +19,7 @@ import { log } from '../../scripts/modules/utils.js'; // Assuming utils.js is in
  * @param {Array<object>} params.messages - Array of message objects (system, user, assistant).
  * @param {number} [params.maxTokens] - Maximum tokens to generate.
  * @param {number} [params.temperature] - Sampling temperature.
+ * @param {string} [params.baseUrl] - Base URL for the OpenRouter API.
  * @returns {Promise<string>} The generated text content.
  * @throws {Error} If the API call fails.
  */
@@ -20,6 +29,7 @@ async function generateOpenRouterText({
 	messages,
 	maxTokens,
 	temperature,
+	baseUrl,
 	...rest // Capture any other Vercel AI SDK compatible parameters
 }) {
 	if (!apiKey) throw new Error('OpenRouter API key is required.');
@@ -28,23 +38,56 @@ async function generateOpenRouterText({
 		throw new Error('Messages array cannot be empty.');
 
 	try {
-		const openrouter = createOpenRouter({ apiKey });
+		const openrouter = getClient(apiKey, baseUrl);
 		const model = openrouter.chat(modelId); // Assuming chat model
 
-		const { text } = await generateText({
+		// Capture the full result from generateText
+		const result = await generateText({
 			model,
 			messages,
 			maxTokens,
 			temperature,
 			...rest // Pass any additional parameters
 		});
-		return text;
+
+		// Check if text and usage are present
+		if (!result || typeof result.text !== 'string') {
+			log(
+				'warn',
+				`OpenRouter generateText for model ${modelId} did not return expected text.`,
+				{ result }
+			);
+			throw new Error('Failed to extract text from OpenRouter response.');
+		}
+		if (!result.usage) {
+			log(
+				'warn',
+				`OpenRouter generateText for model ${modelId} did not return usage data.`,
+				{ result }
+			);
+			// Decide if this is critical. For now, let it pass but telemetry will be incomplete.
+		}
+
+		log('debug', `OpenRouter generateText completed for model ${modelId}`);
+		// Return text and usage
+		return {
+			text: result.text,
+			usage: {
+				inputTokens: result.usage.promptTokens,
+				outputTokens: result.usage.completionTokens
+			}
+		};
 	} catch (error) {
-		log(
+		let detailedMessage = `OpenRouter generateText failed for model ${modelId}: ${error.message}`;
-			'error',
+		if (error.cause) {
-			`OpenRouter generateText failed for model ${modelId}: ${error.message}`
+			detailedMessage += `\n\nCause:\n\n ${typeof error.cause === 'string' ? error.cause : JSON.stringify(error.cause)}`;
-		);
+		}
-		// Re-throw the error for the unified layer to handle retries/fallbacks
+		// Vercel AI SDK sometimes wraps the actual API error response in error.data
+		if (error.data) {
+			detailedMessage += `\n\nData:\n\n ${JSON.stringify(error.data)}`;
+		}
+		// Log the original error object for full context if needed for deeper debugging
+		log('error', detailedMessage, { originalErrorObject: error });
 		throw error;
 	}
 }
@@ -58,6 +101,7 @@ async function generateOpenRouterText({
  * @param {Array<object>} params.messages - Array of message objects (system, user, assistant).
  * @param {number} [params.maxTokens] - Maximum tokens to generate.
  * @param {number} [params.temperature] - Sampling temperature.
+ * @param {string} [params.baseUrl] - Base URL for the OpenRouter API.
  * @returns {Promise<ReadableStream<string>>} A readable stream of text deltas.
  * @throws {Error} If the API call fails.
  */
@@ -67,6 +111,7 @@ async function streamOpenRouterText({
 	messages,
 	maxTokens,
 	temperature,
+	baseUrl,
 	...rest
 }) {
 	if (!apiKey) throw new Error('OpenRouter API key is required.');
@@ -75,7 +120,7 @@ async function streamOpenRouterText({
 		throw new Error('Messages array cannot be empty.');
 
 	try {
-		const openrouter = createOpenRouter({ apiKey });
+		const openrouter = getClient(apiKey, baseUrl);
 		const model = openrouter.chat(modelId);
 
 		// Directly return the stream from the Vercel AI SDK function
@@ -88,10 +133,14 @@ async function streamOpenRouterText({
 		});
 		return stream;
 	} catch (error) {
-		log(
+		let detailedMessage = `OpenRouter streamText failed for model ${modelId}: ${error.message}`;
-			'error',
+		if (error.cause) {
-			`OpenRouter streamText failed for model ${modelId}: ${error.message}`
+			detailedMessage += `\n\nCause:\n\n ${typeof error.cause === 'string' ? error.cause : JSON.stringify(error.cause)}`;
-		);
+		}
+		if (error.data) {
+			detailedMessage += `\n\nData:\n\n ${JSON.stringify(error.data)}`;
+		}
+		log('error', detailedMessage, { originalErrorObject: error });
 		throw error;
 	}
 }
@@ -108,6 +157,7 @@ async function streamOpenRouterText({
  * @param {number} [params.maxRetries=3] - Max retries for object generation.
  * @param {number} [params.maxTokens] - Maximum tokens.
  * @param {number} [params.temperature] - Temperature.
+ * @param {string} [params.baseUrl] - Base URL for the OpenRouter API.
  * @returns {Promise<object>} The generated object matching the schema.
  * @throws {Error} If the API call fails or validation fails.
  */
@@ -120,6 +170,7 @@ async function generateOpenRouterObject({
 	maxRetries = 3,
 	maxTokens,
 	temperature,
+	baseUrl,
 	...rest
 }) {
 	if (!apiKey) throw new Error('OpenRouter API key is required.');
@@ -129,15 +180,15 @@ async function generateOpenRouterObject({
 		throw new Error('Messages array cannot be empty.');
 
 	try {
-		const openrouter = createOpenRouter({ apiKey });
+		const openrouter = getClient(apiKey, baseUrl);
 		const model = openrouter.chat(modelId);
 
-		const { object } = await generateObject({
+		// Capture the full result from generateObject
+		const result = await generateObject({
 			model,
 			schema,
-			mode: 'tool', // Standard mode for most object generation
+			mode: 'tool',
 			tool: {
-				// Define the tool based on the schema
 				name: objectName,
 				description: `Generate an object conforming to the ${objectName} schema.`,
 				parameters: schema
@@ -145,15 +196,45 @@ async function generateOpenRouterObject({
 			messages,
 			maxTokens,
 			temperature,
-			maxRetries, // Pass maxRetries if supported by generateObject
+			maxRetries,
 			...rest
 		});
-		return object;
+
+		// Check if object and usage are present
+		if (!result || typeof result.object === 'undefined') {
+			log(
+				'warn',
+				`OpenRouter generateObject for model ${modelId} did not return expected object.`,
+				{ result }
+			);
+			throw new Error('Failed to extract object from OpenRouter response.');
+		}
+		if (!result.usage) {
+			log(
+				'warn',
+				`OpenRouter generateObject for model ${modelId} did not return usage data.`,
+				{ result }
+			);
+		}
+
+		log('debug', `OpenRouter generateObject completed for model ${modelId}`);
+		// Return object and usage
+		return {
+			object: result.object,
+			usage: {
+				inputTokens: result.usage.promptTokens,
+				outputTokens: result.usage.completionTokens
+			}
+		};
 	} catch (error) {
-		log(
+		let detailedMessage = `OpenRouter generateObject failed for model ${modelId}: ${error.message}`;
-			'error',
+		if (error.cause) {
-			`OpenRouter generateObject failed for model ${modelId}: ${error.message}`
+			detailedMessage += `\n\nCause:\n\n ${typeof error.cause === 'string' ? error.cause : JSON.stringify(error.cause)}`;
-		);
+		}
+		if (error.data) {
+			detailedMessage += `\n\nData:\n\n ${JSON.stringify(error.data)}`;
+		}
+		log('error', detailedMessage, { originalErrorObject: error });
 		throw error;
 	}
 }

src/ai-providers/perplexity.js

@@ -10,13 +10,13 @@ import { log } from '../../scripts/modules/utils.js';
 
 // --- Client Instantiation ---
 // Similar to Anthropic, this expects the resolved API key to be passed in.
-function getClient(apiKey) {
+function getClient(apiKey, baseUrl) {
 	if (!apiKey) {
 		throw new Error('Perplexity API key is required.');
 	}
-	// Create and return a new instance directly
 	return createPerplexity({
-		apiKey: apiKey
+		apiKey: apiKey,
+		...(baseUrl && { baseURL: baseUrl })
 	});
 }
 
@@ -31,6 +31,7 @@ function getClient(apiKey) {
  * @param {Array<object>} params.messages - The messages array.
  * @param {number} [params.maxTokens] - Maximum tokens for the response.
  * @param {number} [params.temperature] - Temperature for generation.
+ * @param {string} [params.baseUrl] - Base URL for the Perplexity API.
  * @returns {Promise<string>} The generated text content.
  * @throws {Error} If the API call fails.
  */
@@ -39,11 +40,12 @@ export async function generatePerplexityText({
 	modelId,
 	messages,
 	maxTokens,
-	temperature
+	temperature,
+	baseUrl
 }) {
 	log('debug', `Generating Perplexity text with model: ${modelId}`);
 	try {
-		const client = getClient(apiKey);
+		const client = getClient(apiKey, baseUrl);
 		const result = await generateText({
 			model: client(modelId),
 			messages: messages,
@@ -54,7 +56,14 @@ export async function generatePerplexityText({
 			'debug',
 			`Perplexity generateText result received. Tokens: ${result.usage.completionTokens}/${result.usage.promptTokens}`
 		);
-		return result.text;
+
+		return {
+			text: result.text,
+			usage: {
+				inputTokens: result.usage.promptTokens,
+				outputTokens: result.usage.completionTokens
+			}
+		};
 	} catch (error) {
 		log('error', `Perplexity generateText failed: ${error.message}`);
 		throw error;
@@ -70,6 +79,7 @@ export async function generatePerplexityText({
  * @param {Array<object>} params.messages - The messages array.
  * @param {number} [params.maxTokens] - Maximum tokens for the response.
  * @param {number} [params.temperature] - Temperature for generation.
+ * @param {string} [params.baseUrl] - Base URL for the Perplexity API.
  * @returns {Promise<object>} The full stream result object from the Vercel AI SDK.
  * @throws {Error} If the API call fails to initiate the stream.
  */
@@ -78,11 +88,12 @@ export async function streamPerplexityText({
 	modelId,
 	messages,
 	maxTokens,
-	temperature
+	temperature,
+	baseUrl
 }) {
 	log('debug', `Streaming Perplexity text with model: ${modelId}`);
 	try {
-		const client = getClient(apiKey);
+		const client = getClient(apiKey, baseUrl);
 		const stream = await streamText({
 			model: client(modelId),
 			messages: messages,
@@ -112,6 +123,7 @@ export async function streamPerplexityText({
  * @param {number} [params.maxTokens] - Maximum tokens for the response.
  * @param {number} [params.temperature] - Temperature for generation.
  * @param {number} [params.maxRetries] - Max retries for validation/generation.
+ * @param {string} [params.baseUrl] - Base URL for the Perplexity API.
  * @returns {Promise<object>} The generated object matching the schema.
  * @throws {Error} If generation or validation fails or is unsupported.
  */
@@ -123,7 +135,8 @@ export async function generatePerplexityObject({
 	objectName = 'generated_object',
 	maxTokens,
 	temperature,
-	maxRetries = 1 // Lower retries as support might be limited
+	maxRetries = 1,
+	baseUrl
 }) {
 	log(
 		'debug',
@@ -134,8 +147,7 @@ export async function generatePerplexityObject({
 		'generateObject support for Perplexity might be limited or experimental.'
 	);
 	try {
-		const client = getClient(apiKey);
+		const client = getClient(apiKey, baseUrl);
-		// Attempt using generateObject, but be prepared for potential issues
 		const result = await generateObject({
 			model: client(modelId),
 			schema: schema,
@@ -148,7 +160,13 @@ export async function generatePerplexityObject({
 			'debug',
 			`Perplexity generateObject result received. Tokens: ${result.usage.completionTokens}/${result.usage.promptTokens}`
 		);
-		return result.object;
+		return {
+			object: result.object,
+			usage: {
+				inputTokens: result.usage.promptTokens,
+				outputTokens: result.usage.completionTokens
+			}
+		};
 	} catch (error) {
 		log(
 			'error',

src/ai-providers/xai.js

@@ -9,14 +9,13 @@ import { generateText, streamText, generateObject } from 'ai'; // Only import wh
 import { log } from '../../scripts/modules/utils.js'; // Assuming utils is accessible
 
 // --- Client Instantiation ---
-function getClient(apiKey) {
+function getClient(apiKey, baseUrl) {
 	if (!apiKey) {
 		throw new Error('xAI API key is required.');
 	}
-	// Create and return a new instance directly
 	return createXai({
-		apiKey: apiKey
+		apiKey: apiKey,
-		// Add baseURL or other options if needed later
+		...(baseUrl && { baseURL: baseUrl })
 	});
 }
 
@@ -31,7 +30,8 @@ function getClient(apiKey) {
  * @param {Array<object>} params.messages - The messages array (e.g., [{ role: 'user', content: '...' }]).
  * @param {number} [params.maxTokens] - Maximum tokens for the response.
  * @param {number} [params.temperature] - Temperature for generation.
- * @returns {Promise<string>} The generated text content.
+ * @param {string} [params.baseUrl] - The base URL for the xAI API.
+ * @returns {Promise<object>} The generated text content and usage.
  * @throws {Error} If the API call fails.
  */
 export async function generateXaiText({
@@ -39,13 +39,14 @@ export async function generateXaiText({
 	modelId,
 	messages,
 	maxTokens,
-	temperature
+	temperature,
+	baseUrl
 }) {
 	log('debug', `Generating xAI text with model: ${modelId}`);
 	try {
-		const client = getClient(apiKey);
+		const client = getClient(apiKey, baseUrl);
 		const result = await generateText({
-			model: client(modelId), // Correct model invocation
+			model: client(modelId),
 			messages: messages,
 			maxTokens: maxTokens,
 			temperature: temperature
@@ -54,7 +55,14 @@ export async function generateXaiText({
 			'debug',
 			`xAI generateText result received. Tokens: ${result.usage.completionTokens}/${result.usage.promptTokens}`
 		);
-		return result.text;
+		// Return text and usage
+		return {
+			text: result.text,
+			usage: {
+				inputTokens: result.usage.promptTokens,
+				outputTokens: result.usage.completionTokens
+			}
+		};
 	} catch (error) {
 		log('error', `xAI generateText failed: ${error.message}`);
 		throw error;
@@ -70,6 +78,7 @@ export async function generateXaiText({
  * @param {Array<object>} params.messages - The messages array.
  * @param {number} [params.maxTokens] - Maximum tokens for the response.
  * @param {number} [params.temperature] - Temperature for generation.
+ * @param {string} [params.baseUrl] - The base URL for the xAI API.
  * @returns {Promise<object>} The full stream result object from the Vercel AI SDK.
  * @throws {Error} If the API call fails to initiate the stream.
  */
@@ -78,18 +87,19 @@ export async function streamXaiText({
 	modelId,
 	messages,
 	maxTokens,
-	temperature
+	temperature,
+	baseUrl
 }) {
 	log('debug', `Streaming xAI text with model: ${modelId}`);
 	try {
-		const client = getClient(apiKey);
+		const client = getClient(apiKey, baseUrl);
 		const stream = await streamText({
-			model: client(modelId), // Correct model invocation
+			model: client(modelId),
 			messages: messages,
 			maxTokens: maxTokens,
 			temperature: temperature
 		});
-		return stream; // Return the full stream object
+		return stream;
 	} catch (error) {
 		log('error', `xAI streamText failed: ${error.message}`, error.stack);
 		throw error;
@@ -110,7 +120,8 @@ export async function streamXaiText({
  * @param {number} [params.maxTokens] - Maximum tokens for the response.
  * @param {number} [params.temperature] - Temperature for generation.
  * @param {number} [params.maxRetries] - Max retries for validation/generation.
- * @returns {Promise<object>} The generated object matching the schema.
+ * @param {string} [params.baseUrl] - The base URL for the xAI API.
+ * @returns {Promise<object>} The generated object matching the schema and its usage.
  * @throws {Error} If generation or validation fails.
  */
 export async function generateXaiObject({
@@ -121,23 +132,25 @@ export async function generateXaiObject({
 	objectName = 'generated_xai_object',
 	maxTokens,
 	temperature,
-	maxRetries = 3
+	maxRetries = 3,
+	baseUrl
 }) {
 	log(
-		'warn', // Log warning as this is likely unsupported
+		'warn',
 		`Attempting to generate xAI object ('${objectName}') with model: ${modelId}. This may not be supported by the provider.`
 	);
 	try {
-		const client = getClient(apiKey);
+		const client = getClient(apiKey, baseUrl);
 		const result = await generateObject({
-			model: client(modelId), // Correct model invocation
+			model: client(modelId),
 			// Note: mode might need adjustment if xAI ever supports object generation differently
 			mode: 'tool',
 			schema: schema,
 			messages: messages,
 			tool: {
 				name: objectName,
-				description: `Generate a ${objectName} based on the prompt.`
+				description: `Generate a ${objectName} based on the prompt.`,
+				parameters: schema
 			},
 			maxTokens: maxTokens,
 			temperature: temperature,
@@ -147,12 +160,19 @@ export async function generateXaiObject({
 			'debug',
 			`xAI generateObject result received. Tokens: ${result.usage.completionTokens}/${result.usage.promptTokens}`
 		);
-		return result.object;
+		// Return object and usage
+		return {
+			object: result.object,
+			usage: {
+				inputTokens: result.usage.promptTokens,
+				outputTokens: result.usage.completionTokens
+			}
+		};
 	} catch (error) {
 		log(
 			'error',
 			`xAI generateObject ('${objectName}') failed: ${error.message}. (Likely unsupported by provider)`
 		);
-		throw error; // Re-throw the error
+		throw error;
 	}
 }

src/constants/task-status.js

@@ -0,0 +1,32 @@
+/**
+ * @typedef {'pending' | 'done' | 'in-progress' | 'review' | 'deferred' | 'cancelled'} TaskStatus
+ */
+
+/**
+ * Task status options list
+ * @type {TaskStatus[]}
+ * @description Defines possible task statuses:
+ * - pending: Task waiting to start
+ * - done: Task completed
+ * - in-progress: Task in progress
+ * - review: Task completed and waiting for review
+ * - deferred: Task postponed or paused
+ * - cancelled: Task cancelled and will not be completed
+ */
+export const TASK_STATUS_OPTIONS = [
+	'pending',
+	'done',
+	'in-progress',
+	'review',
+	'deferred',
+	'cancelled'
+];
+
+/**
+ * Check if a given status is a valid task status
+ * @param {string} status - The status to check
+ * @returns {boolean} True if the status is valid, false otherwise
+ */
+export function isValidTaskStatus(status) {
+	return TASK_STATUS_OPTIONS.includes(status);
+}

src/utils/getVersion.js

@@ -0,0 +1,35 @@
+import fs from 'fs';
+import path from 'path';
+import { fileURLToPath } from 'url';
+import { log } from '../../scripts/modules/utils.js';
+
+/**
+ * Reads the version from the nearest package.json relative to this file.
+ * Returns 'unknown' if not found or on error.
+ * @returns {string} The version string or 'unknown'.
+ */
+export function getTaskMasterVersion() {
+	let version = 'unknown';
+	try {
+		// Get the directory of the current module (getPackageVersion.js)
+		const currentModuleFilename = fileURLToPath(import.meta.url);
+		const currentModuleDirname = path.dirname(currentModuleFilename);
+		// Construct the path to package.json relative to this file (../../package.json)
+		const packageJsonPath = path.join(
+			currentModuleDirname,
+			'..',
+			'..',
+			'package.json'
+		);
+
+		if (fs.existsSync(packageJsonPath)) {
+			const packageJsonContent = fs.readFileSync(packageJsonPath, 'utf8');
+			const packageJson = JSON.parse(packageJsonContent);
+			version = packageJson.version;
+		}
+	} catch (error) {
+		// Silently fall back to default version
+		log('warn', 'Could not read own package.json for version info.', error);
+	}
+	return version;
+}

tasks/task_032.txt

@@ -1,6 +1,6 @@
 # Task ID: 32
 # Title: Implement "learn" Command for Automatic Cursor Rule Generation
-# Status: pending
+# Status: deferred
 # Dependencies: None
 # Priority: high
 # Description: Create a new "learn" command that analyzes Cursor's chat history and code changes to automatically generate or update rule files in the .cursor/rules directory, following the cursor_rules.mdc template format. This command will help Cursor autonomously improve its ability to follow development standards by learning from successful implementations.

tasks/task_043.txt

@@ -1,6 +1,6 @@
 # Task ID: 43
 # Title: Add Research Flag to Add-Task Command
-# Status: pending
+# Status: done
 # Dependencies: None
 # Priority: medium
 # Description: Implement a '--research' flag for the add-task command that enables users to automatically generate research-related subtasks when creating a new task.

tasks/task_044.txt

@@ -48,3 +48,47 @@ Testing should verify both the functionality and security of the webhook system:
 5. Manual verification:
    - Set up integrations with common services (GitHub, Slack, etc.) to verify real-world functionality
    - Verify that the CLI interface for managing webhooks works as expected
+
+# Subtasks:
+## 1. Design webhook registration API endpoints [pending]
+### Dependencies: None
+### Description: Create API endpoints for registering, updating, and deleting webhook subscriptions
+### Details:
+Implement RESTful API endpoints that allow clients to register webhook URLs, specify event types they want to subscribe to, and manage their subscriptions. Include validation for URL format, required parameters, and authentication requirements.
+
+## 2. Implement webhook authentication and security measures [pending]
+### Dependencies: 44.1
+### Description: Develop security mechanisms for webhook verification and payload signing
+### Details:
+Implement signature verification using HMAC, rate limiting to prevent abuse, IP whitelisting options, and webhook secret management. Create a secure token system for webhook verification and implement TLS for all webhook communications.
+
+## 3. Create event trigger definition interface [pending]
+### Dependencies: None
+### Description: Design and implement the interface for defining event triggers and conditions
+### Details:
+Develop a user interface or API that allows defining what events should trigger webhooks. Include support for conditional triggers based on event properties, filtering options, and the ability to specify payload formats.
+
+## 4. Build event processing and queuing system [pending]
+### Dependencies: 44.1, 44.3
+### Description: Implement a robust system for processing and queuing events before webhook delivery
+### Details:
+Create an event queue using a message broker (like RabbitMQ or Kafka) to handle high volumes of events. Implement event deduplication, prioritization, and persistence to ensure reliable delivery even during system failures.
+
+## 5. Develop webhook delivery and retry mechanism [pending]
+### Dependencies: 44.2, 44.4
+### Description: Create a reliable system for webhook delivery with retry logic and failure handling
+### Details:
+Implement exponential backoff retry logic, configurable retry attempts, and dead letter queues for failed deliveries. Add monitoring for webhook delivery success rates and performance metrics. Include timeout handling for unresponsive webhook endpoints.
+
+## 6. Implement comprehensive error handling and logging [pending]
+### Dependencies: 44.5
+### Description: Create robust error handling, logging, and monitoring for the webhook system
+### Details:
+Develop detailed error logging for webhook failures, including response codes, error messages, and timing information. Implement alerting for critical failures and create a dashboard for monitoring system health. Add debugging tools for webhook delivery issues.
+
+## 7. Create webhook testing and simulation tools [pending]
+### Dependencies: 44.3, 44.5, 44.6
+### Description: Develop tools for testing webhook integrations and simulating event triggers
+### Details:
+Build a webhook testing console that allows manual triggering of events, viewing delivery history, and replaying failed webhooks. Create a webhook simulator for developers to test their endpoint implementations without generating real system events.
+

tasks/task_045.txt

@@ -53,3 +53,35 @@ Testing should cover the following scenarios:
    - Test the interaction with other flags and commands
 
 Create mock GitHub API responses for testing to avoid hitting rate limits during development and testing. Use environment variables to configure test credentials if needed.
+
+# Subtasks:
+## 1. Design GitHub API integration architecture [pending]
+### Dependencies: None
+### Description: Create a technical design document outlining the architecture for GitHub API integration, including authentication flow, rate limiting considerations, and error handling strategies.
+### Details:
+Document should include: API endpoints to be used, authentication method (OAuth vs Personal Access Token), data flow diagrams, and security considerations. Research GitHub API rate limits and implement appropriate throttling mechanisms.
+
+## 2. Implement GitHub URL parsing and validation [pending]
+### Dependencies: 45.1
+### Description: Create a module to parse and validate GitHub issue URLs, extracting repository owner, repository name, and issue number.
+### Details:
+Handle various GitHub URL formats (e.g., github.com/owner/repo/issues/123, github.com/owner/repo/pull/123). Implement validation to ensure the URL points to a valid issue or pull request. Return structured data with owner, repo, and issue number for valid URLs.
+
+## 3. Develop GitHub API client for issue fetching [pending]
+### Dependencies: 45.1, 45.2
+### Description: Create a service to authenticate with GitHub and fetch issue details using the GitHub REST API.
+### Details:
+Implement authentication using GitHub Personal Access Tokens or OAuth. Handle API responses, including error cases (rate limiting, authentication failures, not found). Extract relevant issue data: title, description, labels, assignees, and comments.
+
+## 4. Create task formatter for GitHub issues [pending]
+### Dependencies: 45.3
+### Description: Develop a formatter to convert GitHub issue data into the application's task format.
+### Details:
+Map GitHub issue fields to task fields (title, description, etc.). Convert GitHub markdown to the application's supported format. Handle special GitHub features like issue references and user mentions. Generate appropriate tags based on GitHub labels.
+
+## 5. Implement end-to-end import flow with UI [pending]
+### Dependencies: 45.4
+### Description: Create the user interface and workflow for importing GitHub issues, including progress indicators and error handling.
+### Details:
+Design and implement UI for URL input and import confirmation. Show loading states during API calls. Display meaningful error messages for various failure scenarios. Allow users to review and modify imported task details before saving. Add automated tests for the entire import flow.
+

tasks/task_046.txt

@@ -53,3 +53,35 @@ The command should follow the same design patterns as `analyze-complexity` for c
    - The ranking should prioritize high-impact, high-confidence, easy-to-implement tasks
    - Performance should be acceptable even with a large number of tasks
    - The command should handle edge cases gracefully (empty projects, missing data)
+
+# Subtasks:
+## 1. Design ICE scoring algorithm [pending]
+### Dependencies: None
+### Description: Create the algorithm for calculating Impact, Confidence, and Ease scores for tasks
+### Details:
+Define the mathematical formula for ICE scoring (Impact × Confidence × Ease). Determine the scale for each component (e.g., 1-10). Create rules for how AI will evaluate each component based on task attributes like complexity, dependencies, and descriptions. Document the scoring methodology for future reference.
+
+## 2. Implement AI integration for ICE scoring [pending]
+### Dependencies: 46.1
+### Description: Develop the AI component that will analyze tasks and generate ICE scores
+### Details:
+Create prompts for the AI to evaluate Impact, Confidence, and Ease. Implement error handling for AI responses. Add caching to prevent redundant AI calls. Ensure the AI provides justification for each score component. Test with various task types to ensure consistent scoring.
+
+## 3. Create report file generator [pending]
+### Dependencies: 46.2
+### Description: Build functionality to generate a structured report file with ICE analysis results
+### Details:
+Design the report file format (JSON, CSV, or Markdown). Implement sorting of tasks by ICE score. Include task details, individual I/C/E scores, and final ICE score in the report. Add timestamp and project metadata. Create a function to save the report to the specified location.
+
+## 4. Implement CLI rendering for ICE analysis [pending]
+### Dependencies: 46.3
+### Description: Develop the command-line interface for displaying ICE analysis results
+### Details:
+Design a tabular format for displaying ICE scores in the terminal. Use color coding to highlight high/medium/low priority tasks. Implement filtering options (by score range, task type, etc.). Add sorting capabilities. Create a summary view that shows top N tasks by ICE score.
+
+## 5. Integrate with existing complexity reports [pending]
+### Dependencies: 46.3, 46.4
+### Description: Connect the ICE analysis functionality with the existing complexity reporting system
+### Details:
+Modify the existing complexity report to include ICE scores. Ensure consistent formatting between complexity and ICE reports. Add cross-referencing between reports. Update the command-line help documentation. Test the integrated system with various project sizes and configurations.
+

tasks/task_047.txt

@@ -64,3 +64,41 @@ Testing should verify the complete workflow functions correctly:
 5. Regression Testing:
    - Verify that existing functionality continues to work
    - Ensure compatibility with keyboard shortcuts and accessibility features
+
+# Subtasks:
+## 1. Design Task Expansion UI Components [pending]
+### Dependencies: None
+### Description: Create UI components for the expanded task suggestion actions card that allow for task breakdown and additional context input.
+### Details:
+Design mockups for expanded card view, including subtask creation interface, context input fields, and task management controls. Ensure the design is consistent with existing UI patterns and responsive across different screen sizes. Include animations for card expansion/collapse.
+
+## 2. Implement State Management for Task Expansion [pending]
+### Dependencies: 47.1
+### Description: Develop the state management logic to handle expanded task states, subtask creation, and context additions.
+### Details:
+Create state handlers for expanded/collapsed states, subtask array management, and context data. Implement proper validation for user inputs and error handling. Ensure state persistence across user sessions and synchronization with backend services.
+
+## 3. Build Context Addition Functionality [pending]
+### Dependencies: 47.2
+### Description: Create the functionality that allows users to add additional context to tasks and subtasks.
+### Details:
+Implement context input fields with support for rich text, attachments, links, and references to other tasks. Add auto-save functionality for context changes and version history if applicable. Include context suggestion features based on task content.
+
+## 4. Develop Task Management Controls [pending]
+### Dependencies: 47.2
+### Description: Implement controls for managing tasks within the expanded card view, including prioritization, scheduling, and assignment.
+### Details:
+Create UI controls for task prioritization (drag-and-drop ranking), deadline setting with calendar integration, assignee selection with user search, and status updates. Implement notification triggers for task changes and deadline reminders.
+
+## 5. Integrate with Existing Task Systems [pending]
+### Dependencies: 47.3, 47.4
+### Description: Ensure the enhanced actions card workflow integrates seamlessly with existing task management functionality.
+### Details:
+Connect the new UI components to existing backend APIs. Update data models if necessary to support new features. Ensure compatibility with existing task filters, search, and reporting features. Implement data migration plan for existing tasks if needed.
+
+## 6. Test and Optimize User Experience [pending]
+### Dependencies: 47.5
+### Description: Conduct thorough testing of the enhanced workflow and optimize based on user feedback and performance metrics.
+### Details:
+Perform usability testing with representative users. Collect metrics on task completion time, error rates, and user satisfaction. Optimize performance for large task lists and complex subtask hierarchies. Implement A/B testing for alternative UI approaches if needed.
+

tasks/task_048.txt

@@ -42,3 +42,23 @@ Testing should verify that the refactoring maintains identical functionality whi
 4. Documentation:
    - Verify documentation is updated to reflect the new prompt organization
    - Confirm the index.js export pattern works as expected for importing prompts
+
+# Subtasks:
+## 1. Create prompts directory structure [pending]
+### Dependencies: None
+### Description: Create a centralized 'prompts' directory with appropriate subdirectories for different prompt categories
+### Details:
+Create a 'prompts' directory at the project root. Within this directory, create subdirectories based on functional categories (e.g., 'core', 'agents', 'utils'). Add an index.js file in each subdirectory to facilitate imports. Create a root index.js file that re-exports all prompts for easy access.
+
+## 2. Extract prompts into individual files [pending]
+### Dependencies: 48.1
+### Description: Identify all hardcoded prompts in the codebase and extract them into individual files in the prompts directory
+### Details:
+Search through the codebase for all hardcoded prompt strings. For each prompt, create a new file in the appropriate subdirectory with a descriptive name (e.g., 'taskBreakdownPrompt.js'). Format each file to export the prompt string as a constant. Add JSDoc comments to document the purpose and expected usage of each prompt.
+
+## 3. Update functions to import prompts [pending]
+### Dependencies: 48.1, 48.2
+### Description: Modify all functions that use hardcoded prompts to import them from the centralized structure
+### Details:
+For each function that previously used a hardcoded prompt, add an import statement to pull in the prompt from the centralized structure. Test each function after modification to ensure it still works correctly. Update any tests that might be affected by the refactoring. Create a pull request with the changes and document the new prompt structure in the project documentation.
+

tasks/task_049.txt

@@ -64,3 +64,41 @@ Testing should verify all aspects of the code analysis command:
    - Generated recommendations are specific and actionable
    - Created tasks follow the project's task format standards
    - Analysis results are consistent across multiple runs on the same codebase
+
+# Subtasks:
+## 1. Design pattern recognition algorithm [pending]
+### Dependencies: None
+### Description: Create an algorithm to identify common code patterns and anti-patterns in the codebase
+### Details:
+Develop a system that can scan code files and identify common design patterns (Factory, Singleton, etc.) and anti-patterns (God objects, excessive coupling, etc.). Include detection for language-specific patterns and create a classification system for identified patterns.
+
+## 2. Implement best practice verification [pending]
+### Dependencies: 49.1
+### Description: Build verification checks against established coding standards and best practices
+### Details:
+Create a framework to compare code against established best practices for the specific language/framework. Include checks for naming conventions, function length, complexity metrics, comment coverage, and other industry-standard quality indicators.
+
+## 3. Develop AI integration for code analysis [pending]
+### Dependencies: 49.1, 49.2
+### Description: Integrate AI capabilities to enhance code analysis and provide intelligent recommendations
+### Details:
+Connect to AI services (like OpenAI) to analyze code beyond rule-based checks. Configure the AI to understand context, project-specific patterns, and provide nuanced analysis that rule-based systems might miss.
+
+## 4. Create recommendation generation system [pending]
+### Dependencies: 49.2, 49.3
+### Description: Build a system to generate actionable improvement recommendations based on analysis results
+### Details:
+Develop algorithms to transform analysis results into specific, actionable recommendations. Include priority levels, effort estimates, and potential impact assessments for each recommendation.
+
+## 5. Implement task creation functionality [pending]
+### Dependencies: 49.4
+### Description: Add capability to automatically create tasks from code quality recommendations
+### Details:
+Build functionality to convert recommendations into tasks in the project management system. Include appropriate metadata, assignee suggestions based on code ownership, and integration with existing workflow systems.
+
+## 6. Create comprehensive reporting interface [pending]
+### Dependencies: 49.4, 49.5
+### Description: Develop a user interface to display analysis results and recommendations
+### Details:
+Build a dashboard showing code quality metrics, identified patterns, recommendations, and created tasks. Include filtering options, trend analysis over time, and the ability to drill down into specific issues with code snippets and explanations.
+

tasks/task_052.txt

@@ -49,3 +49,35 @@ Testing should verify both the functionality and user experience of the suggest-
    - Test with extremely large numbers of existing tasks
 
 Manually verify the command produces contextually appropriate suggestions that align with the project's current state and needs.
+
+# Subtasks:
+## 1. Design data collection mechanism for existing tasks [pending]
+### Dependencies: None
+### Description: Create a module to collect and format existing task data from the system for AI processing
+### Details:
+Implement a function that retrieves all existing tasks from storage, formats them appropriately for AI context, and handles edge cases like empty task lists or corrupted data. Include metadata like task status, dependencies, and creation dates to provide rich context for suggestions.
+
+## 2. Implement AI integration for task suggestions [pending]
+### Dependencies: 52.1
+### Description: Develop the core functionality to generate task suggestions using AI based on existing tasks
+### Details:
+Create an AI prompt template that effectively communicates the existing task context and request for suggestions. Implement error handling for API failures, rate limiting, and malformed responses. Include parameters for controlling suggestion quantity and specificity.
+
+## 3. Build interactive CLI interface for suggestions [pending]
+### Dependencies: 52.2
+### Description: Create the command-line interface for requesting and displaying task suggestions
+### Details:
+Design a user-friendly CLI command structure with appropriate flags for customization. Implement progress indicators during AI processing and format the output of suggestions in a clear, readable format. Include help text and examples in the command documentation.
+
+## 4. Implement suggestion selection and task creation [pending]
+### Dependencies: 52.3
+### Description: Allow users to interactively select suggestions to convert into actual tasks
+### Details:
+Create an interactive selection interface where users can review suggestions, select which ones to create as tasks, and optionally modify them before creation. Implement batch creation capabilities and validation to ensure new tasks meet system requirements.
+
+## 5. Add configuration options and flag handling [pending]
+### Dependencies: 52.3, 52.4
+### Description: Implement various configuration options and command flags for customizing suggestion behavior
+### Details:
+Create a comprehensive set of command flags for controlling suggestion quantity, specificity, format, and other parameters. Implement persistent configuration options that users can set as defaults. Document all available options and provide examples of common usage patterns.
+

tasks/task_055.txt

@@ -48,3 +48,35 @@ Testing should verify both the new positional argument functionality and continu
    - Verify examples in documentation show both styles where appropriate
 
 All tests should pass with 100% of commands supporting both argument styles without any regression in existing functionality.
+
+# Subtasks:
+## 1. Analyze current CLI argument parsing structure [pending]
+### Dependencies: None
+### Description: Review the existing CLI argument parsing code to understand how arguments are currently processed and identify integration points for positional arguments.
+### Details:
+Document the current argument parsing flow, identify key classes and methods responsible for argument handling, and determine how named arguments are currently processed. Create a technical design document outlining the current architecture and proposed changes.
+
+## 2. Design positional argument specification format [pending]
+### Dependencies: 55.1
+### Description: Create a specification for how positional arguments will be defined in command definitions, including their order, required/optional status, and type validation.
+### Details:
+Define a clear syntax for specifying positional arguments in command definitions. Consider how to handle mixed positional and named arguments, default values, and type constraints. Document the specification with examples for different command types.
+
+## 3. Implement core positional argument parsing logic [pending]
+### Dependencies: 55.1, 55.2
+### Description: Modify the argument parser to recognize and process positional arguments according to the specification, while maintaining compatibility with existing named arguments.
+### Details:
+Update the parser to identify arguments without flags as positional, map them to the correct parameter based on order, and apply appropriate validation. Ensure the implementation handles missing required positional arguments and provides helpful error messages.
+
+## 4. Handle edge cases and error conditions [pending]
+### Dependencies: 55.3
+### Description: Implement robust handling for edge cases such as too many/few arguments, type mismatches, and ambiguous situations between positional and named arguments.
+### Details:
+Create comprehensive error handling for scenarios like: providing both positional and named version of the same argument, incorrect argument types, missing required positional arguments, and excess positional arguments. Ensure error messages are clear and actionable for users.
+
+## 5. Update documentation and create usage examples [pending]
+### Dependencies: 55.2, 55.3, 55.4
+### Description: Update CLI documentation to explain positional argument support and provide clear examples showing how to use positional arguments with different commands.
+### Details:
+Revise user documentation to include positional argument syntax, update command reference with positional argument information, and create example command snippets showing both positional and named argument usage. Include a migration guide for users transitioning from named-only to positional arguments.
+

tasks/task_057.txt

@@ -65,3 +65,41 @@ Acceptance Criteria:
 - Help text is comprehensive and includes examples
 - Interface is visually consistent across all commands
 - Tool remains fully functional in non-interactive environments
+
+# Subtasks:
+## 1. Implement Configurable Log Levels [pending]
+### Dependencies: None
+### Description: Create a logging system with different verbosity levels that users can configure
+### Details:
+Design and implement a logging system with at least 4 levels (ERROR, WARNING, INFO, DEBUG). Add command-line options to set the verbosity level. Ensure logs are color-coded by severity and can be redirected to files. Include timestamp formatting options.
+
+## 2. Design Terminal Color Scheme and Visual Elements [pending]
+### Dependencies: None
+### Description: Create a consistent and accessible color scheme for the CLI interface
+### Details:
+Define a color palette that works across different terminal environments. Implement color-coding for different task states, priorities, and command categories. Add support for terminals without color capabilities. Design visual separators, headers, and footers for different output sections.
+
+## 3. Implement Progress Indicators and Loading Animations [pending]
+### Dependencies: 57.2
+### Description: Add visual feedback for long-running operations
+### Details:
+Create spinner animations for operations that take time to complete. Implement progress bars for operations with known completion percentages. Ensure animations degrade gracefully in terminals with limited capabilities. Add estimated time remaining calculations where possible.
+
+## 4. Develop Interactive Selection Menus [pending]
+### Dependencies: 57.2
+### Description: Create interactive menus for task selection and configuration
+### Details:
+Implement arrow-key navigation for selecting tasks from a list. Add checkbox and radio button interfaces for multi-select and single-select options. Include search/filter functionality for large task lists. Ensure keyboard shortcuts are consistent and documented.
+
+## 5. Design Tabular and Structured Output Formats [pending]
+### Dependencies: 57.2
+### Description: Improve the formatting of task lists and detailed information
+### Details:
+Create table layouts with proper column alignment for task lists. Implement tree views for displaying task hierarchies and dependencies. Add support for different output formats (plain text, JSON, CSV). Ensure outputs are properly paginated for large datasets.
+
+## 6. Create Help System and Interactive Documentation [pending]
+### Dependencies: 57.2, 57.4, 57.5
+### Description: Develop an in-CLI help system with examples and contextual assistance
+### Details:
+Implement a comprehensive help command with examples for each feature. Add contextual help that suggests relevant commands based on user actions. Create interactive tutorials for new users. Include command auto-completion suggestions and syntax highlighting for command examples.
+

tasks/task_060.txt

@@ -71,3 +71,47 @@ Ensure all commands have proper help text and error handling for cases like no m
    - Verify the personality simulation is consistent and believable
    - Test the round-table output file readability and usefulness
    - Verify that using round-table output to update tasks produces meaningful improvements
+
+# Subtasks:
+## 1. Design Mentor System Architecture [pending]
+### Dependencies: None
+### Description: Create a comprehensive architecture for the mentor system, defining data models, relationships, and interaction patterns.
+### Details:
+Define mentor profiles structure, expertise categorization, availability tracking, and relationship to user accounts. Design the database schema for storing mentor information and interactions. Create flowcharts for mentor-mentee matching algorithms and interaction workflows.
+
+## 2. Implement Mentor Profile Management [pending]
+### Dependencies: 60.1
+### Description: Develop the functionality for creating, editing, and managing mentor profiles in the system.
+### Details:
+Build UI components for mentor profile creation and editing. Implement backend APIs for profile CRUD operations. Create expertise tagging system and availability calendar. Add profile verification and approval workflows for quality control.
+
+## 3. Develop Round-Table Discussion Framework [pending]
+### Dependencies: 60.1
+### Description: Create the core framework for hosting and managing round-table discussions between mentors and users.
+### Details:
+Design the discussion room data model and state management. Implement discussion scheduling and participant management. Create discussion topic and agenda setting functionality. Develop discussion moderation tools and rules enforcement mechanisms.
+
+## 4. Implement LLM Integration for AI Mentors [pending]
+### Dependencies: 60.3
+### Description: Integrate LLM capabilities to simulate AI mentors that can participate in round-table discussions.
+### Details:
+Select appropriate LLM models for mentor simulation. Develop prompt engineering templates for different mentor personas and expertise areas. Implement context management to maintain conversation coherence. Create fallback mechanisms for handling edge cases in discussions.
+
+## 5. Build Discussion Output Formatter [pending]
+### Dependencies: 60.3, 60.4
+### Description: Create a system to format and present round-table discussion outputs in a structured, readable format.
+### Details:
+Design templates for discussion summaries and transcripts. Implement real-time formatting of ongoing discussions. Create exportable formats for discussion outcomes (PDF, markdown, etc.). Develop highlighting and annotation features for key insights.
+
+## 6. Integrate Mentor System with Task Management [pending]
+### Dependencies: 60.2, 60.3
+### Description: Connect the mentor system with the existing task management functionality to enable task-specific mentoring.
+### Details:
+Create APIs to link tasks with relevant mentors based on expertise. Implement functionality to initiate discussions around specific tasks. Develop mechanisms for mentors to provide feedback and guidance on tasks. Build notification system for task-related mentor interactions.
+
+## 7. Test and Optimize Round-Table Discussions [pending]
+### Dependencies: 60.4, 60.5, 60.6
+### Description: Conduct comprehensive testing of the round-table discussion feature and optimize for performance and user experience.
+### Details:
+Perform load testing with multiple concurrent discussions. Test AI mentor responses for quality and relevance. Optimize LLM usage for cost efficiency. Conduct user testing sessions and gather feedback. Implement performance monitoring and analytics for ongoing optimization.
+

tasks/task_061.txt

@@ -1,6 +1,6 @@
 # Task ID: 61
 # Title: Implement Flexible AI Model Management
-# Status: in-progress
+# Status: done
 # Dependencies: None
 # Priority: high
 # Description: Currently, Task Master only supports Claude for main operations and Perplexity for research. Users are limited in flexibility when managing AI models. Adding comprehensive support for multiple popular AI models (OpenAI, Ollama, Gemini, OpenRouter, Grok) and providing intuitive CLI commands for model management will significantly enhance usability, transparency, and adaptability to user preferences and project-specific needs. This task will now leverage Vercel's AI SDK to streamline integration and management of these models.
@@ -486,7 +486,7 @@ The existing `ai-services.js` should be refactored to:
 7. Add verbose output option for debugging
 8. Testing approach: Create integration tests that verify model setting functionality with various inputs
 
-## 8. Update Main Task Processing Logic [deferred]
+## 8. Update Main Task Processing Logic [done]
 ### Dependencies: 61.4, 61.5, 61.18
 ### Description: Refactor the main task processing logic to use the new AI services module and support dynamic model selection.
 ### Details:
@@ -554,7 +554,7 @@ When updating the main task processing logic, implement the following changes to
    ```
 </info added on 2025-04-20T03:55:56.310Z>
 
-## 9. Update Research Processing Logic [deferred]
+## 9. Update Research Processing Logic [done]
 ### Dependencies: 61.4, 61.5, 61.8, 61.18
 ### Description: Refactor the research processing logic to use the new AI services module and support dynamic model selection for research operations.
 ### Details:
@@ -747,7 +747,7 @@ const result = await generateObjectService({
 5. Ensure any default values previously hardcoded are now retrieved from the configuration system.
 </info added on 2025-04-20T03:55:01.707Z>
 
-## 12. Refactor Basic Subtask Generation to use generateObjectService [cancelled]
+## 12. Refactor Basic Subtask Generation to use generateObjectService [done]
 ### Dependencies: 61.23
 ### Description: Update the `generateSubtasks` function in `ai-services.js` to use the new `generateObjectService` from `ai-services-unified.js` with a Zod schema for the subtask array.
 ### Details:
@@ -798,7 +798,7 @@ The refactoring should leverage the new configuration system:
    ```
 </info added on 2025-04-20T03:54:45.542Z>
 
-## 13. Refactor Research Subtask Generation to use generateObjectService [cancelled]
+## 13. Refactor Research Subtask Generation to use generateObjectService [done]
 ### Dependencies: 61.23
 ### Description: Update the `generateSubtasksWithPerplexity` function in `ai-services.js` to first perform research (potentially keeping the Perplexity call separate or adapting it) and then use `generateObjectService` from `ai-services-unified.js` with research results included in the prompt.
 ### Details:
@@ -828,7 +828,7 @@ const { verbose } = getLoggingConfig();
 5. Ensure the transition to generateObjectService maintains all existing functionality while leveraging the new configuration system
 </info added on 2025-04-20T03:54:26.882Z>
 
-## 14. Refactor Research Task Description Generation to use generateObjectService [cancelled]
+## 14. Refactor Research Task Description Generation to use generateObjectService [done]
 ### Dependencies: 61.23
 ### Description: Update the `generateTaskDescriptionWithPerplexity` function in `ai-services.js` to first perform research and then use `generateObjectService` from `ai-services-unified.js` to generate the structured task description.
 ### Details:
@@ -869,7 +869,7 @@ return generateObjectService({
 5. Remove any hardcoded configuration values, ensuring all settings are retrieved from the centralized configuration system.
 </info added on 2025-04-20T03:54:04.420Z>
 
-## 15. Refactor Complexity Analysis AI Call to use generateObjectService [cancelled]
+## 15. Refactor Complexity Analysis AI Call to use generateObjectService [done]
 ### Dependencies: 61.23
 ### Description: Update the logic that calls the AI after using `generateComplexityAnalysisPrompt` in `ai-services.js` to use the new `generateObjectService` from `ai-services-unified.js` with a Zod schema for the complexity report.
 ### Details:
@@ -916,7 +916,7 @@ The complexity analysis AI call should be updated to align with the new configur
    ```
 </info added on 2025-04-20T03:53:46.120Z>
 
-## 16. Refactor Task Addition AI Call to use generateObjectService [cancelled]
+## 16. Refactor Task Addition AI Call to use generateObjectService [done]
 ### Dependencies: 61.23
 ### Description: Update the logic that calls the AI after using `_buildAddTaskPrompt` in `ai-services.js` to use the new `generateObjectService` from `ai-services-unified.js` with a Zod schema for the single task object.
 ### Details:
@@ -961,7 +961,7 @@ To implement this refactoring, you'll need to:
 4. Update any error handling to match the new service's error patterns.
 </info added on 2025-04-20T03:53:27.455Z>
 
-## 17. Refactor General Chat/Update AI Calls [deferred]
+## 17. Refactor General Chat/Update AI Calls [done]
 ### Dependencies: 61.23
 ### Description: Refactor functions like `sendChatWithContext` (and potentially related task update functions in `task-manager.js` if they make direct AI calls) to use `streamTextService` or `generateTextService` from `ai-services-unified.js`.
 ### Details:
@@ -1008,7 +1008,7 @@ When refactoring `sendChatWithContext` and related functions, ensure they align
 5. Ensure any default behaviors respect configuration defaults rather than hardcoded values.
 </info added on 2025-04-20T03:53:03.709Z>
 
-## 18. Refactor Callers of AI Parsing Utilities [deferred]
+## 18. Refactor Callers of AI Parsing Utilities [done]
 ### Dependencies: None
 ### Description: Update the code that calls `parseSubtasksFromText`, `parseTaskJsonResponse`, and `parseTasksFromCompletion` to instead directly handle the structured JSON output provided by `generateObjectService` (as the refactored AI calls will now use it).
 ### Details:
@@ -1761,19 +1761,19 @@ export async function generateGoogleObject({
 ```
 </info added on 2025-04-27T00:00:46.675Z>
 
-## 25. Implement `ollama.js` Provider Module [pending]
+## 25. Implement `ollama.js` Provider Module [done]
 ### Dependencies: None
 ### Description: Create and implement the `ollama.js` module within `src/ai-providers/`. This module should contain functions to interact with local Ollama models using the **`ollama-ai-provider` library**, adhering to the standardized input/output format defined for `ai-services-unified.js`. Note the specific library used.
 ### Details:
 
 
-## 26. Implement `mistral.js` Provider Module using Vercel AI SDK [pending]
+## 26. Implement `mistral.js` Provider Module using Vercel AI SDK [done]
 ### Dependencies: None
 ### Description: Create and implement the `mistral.js` module within `src/ai-providers/`. This module should contain functions to interact with Mistral AI models using the **Vercel AI SDK (`@ai-sdk/mistral`)**, adhering to the standardized input/output format defined for `ai-services-unified.js`.
 ### Details:
 
 
-## 27. Implement `azure.js` Provider Module using Vercel AI SDK [pending]
+## 27. Implement `azure.js` Provider Module using Vercel AI SDK [done]
 ### Dependencies: None
 ### Description: Create and implement the `azure.js` module within `src/ai-providers/`. This module should contain functions to interact with Azure OpenAI models using the **Vercel AI SDK (`@ai-sdk/azure`)**, adhering to the standardized input/output format defined for `ai-services-unified.js`.
 ### Details:
@@ -2649,13 +2649,13 @@ Here are more detailed steps for removing unnecessary console logs:
 10. After committing changes, monitor the application in staging environment to ensure no critical information is lost.
 </info added on 2025-05-02T20:47:56.080Z>
 
-## 44. Add setters for temperature, max tokens on per role basis. [pending]
+## 44. Add setters for temperature, max tokens on per role basis. [done]
 ### Dependencies: None
 ### Description: NOT per model/provider basis though we could probably just define those in the .taskmasterconfig file but then they would be hard-coded. if we let users define them on a per role basis, they will define incorrect values. maybe a good middle ground is to do both - we enforce maximum using known max tokens for input and output at the .taskmasterconfig level but then we also give setters to adjust temp/input tokens/output tokens for each of the 3 roles.
 ### Details:
 
 
-## 45. Add support for Bedrock provider with ai sdk and unified service [pending]
+## 45. Add support for Bedrock provider with ai sdk and unified service [done]
 ### Dependencies: None
 ### Description: 
 ### Details:

tasks/task_065.txt

@@ -9,3 +9,41 @@ Update the Taskmaster installation scripts and documentation to support Bun as a
 
 # Test Strategy:
 1. Install Taskmaster using Bun on macOS, Linux, and Windows (including WSL and PowerShell), following the updated documentation. 2. Run the full installation and initialization process, verifying that the directory structure, templates, and MCP config are set up identically to npm, pnpm, and Yarn. 3. Execute all CLI commands (including 'init') and confirm functional parity. 4. If a website or account setup is required, test these flows for consistency; if not, confirm and document this. 5. Check for Bun-specific issues (e.g., install hangs) and verify that troubleshooting steps are effective. 6. Ensure the documentation is clear, accurate, and up to date for all supported platforms.
+
+# Subtasks:
+## 1. Research Bun compatibility requirements [pending]
+### Dependencies: None
+### Description: Investigate Bun's JavaScript runtime environment and identify key differences from Node.js that may affect Taskmaster's installation and operation.
+### Details:
+Research Bun's package management, module resolution, and API compatibility with Node.js. Document any potential issues or limitations that might affect Taskmaster. Identify required changes to make Taskmaster compatible with Bun's execution model.
+
+## 2. Update installation scripts for Bun compatibility [pending]
+### Dependencies: 65.1
+### Description: Modify the existing installation scripts to detect and support Bun as a runtime environment.
+### Details:
+Add Bun detection logic to installation scripts. Update package management commands to use Bun equivalents where needed. Ensure all dependencies are compatible with Bun. Modify any Node.js-specific code to work with Bun's runtime.
+
+## 3. Create Bun-specific installation path [pending]
+### Dependencies: 65.2
+### Description: Implement a dedicated installation flow for Bun users that optimizes for Bun's capabilities.
+### Details:
+Create a Bun-specific installation script that leverages Bun's performance advantages. Update any environment detection logic to properly identify Bun environments. Ensure proper path resolution and environment variable handling for Bun.
+
+## 4. Test Taskmaster installation with Bun [pending]
+### Dependencies: 65.3
+### Description: Perform comprehensive testing of the installation process using Bun across different operating systems.
+### Details:
+Test installation on Windows, macOS, and Linux using Bun. Verify that all Taskmaster features work correctly when installed via Bun. Document any issues encountered and implement fixes as needed.
+
+## 5. Test Taskmaster operation with Bun [pending]
+### Dependencies: 65.4
+### Description: Ensure all Taskmaster functionality works correctly when running under Bun.
+### Details:
+Test all Taskmaster commands and features when running with Bun. Compare performance metrics between Node.js and Bun. Identify and fix any runtime issues specific to Bun. Ensure all plugins and extensions are compatible.
+
+## 6. Update documentation for Bun support [pending]
+### Dependencies: 65.4, 65.5
+### Description: Update all relevant documentation to include information about installing and running Taskmaster with Bun.
+### Details:
+Add Bun installation instructions to README and documentation. Document any Bun-specific considerations or limitations. Update troubleshooting guides to include Bun-specific issues. Create examples showing Bun usage with Taskmaster.
+

tasks/task_066.txt

@@ -1,6 +1,6 @@
 # Task ID: 66
 # Title: Support Status Filtering in Show Command for Subtasks
-# Status: pending
+# Status: done
 # Dependencies: None
 # Priority: medium
 # Description: Enhance the 'show' command to accept a status parameter that filters subtasks by their current status, allowing users to view only subtasks matching a specific status.

tasks/task_068.txt

@@ -9,3 +9,17 @@
 
 # Test Strategy:
 
+
+# Subtasks:
+## 1. Design task creation form without PRD [pending]
+### Dependencies: None
+### Description: Create a user interface form that allows users to manually input task details without requiring a PRD document
+### Details:
+Design a form with fields for task title, description, priority, assignee, due date, and other relevant task attributes. Include validation to ensure required fields are completed. The form should be intuitive and provide clear guidance on how to create a task manually.
+
+## 2. Implement task saving functionality [pending]
+### Dependencies: 68.1
+### Description: Develop the backend functionality to save manually created tasks to the database
+### Details:
+Create API endpoints to handle task creation requests from the frontend. Implement data validation, error handling, and confirmation messages. Ensure the saved tasks appear in the task list view and can be edited or deleted like PRD-parsed tasks.
+

tasks/task_069.txt

@@ -57,3 +57,29 @@ Implementation Plan:
     *   Call `analyze_project_complexity` tool without `ids`. Verify full analysis and merging.
 3.  Verify report `meta` section is updated correctly on each run.
 
+
+# Subtasks:
+## 1. Modify core complexity analysis logic [pending]
+### Dependencies: None
+### Description: Update the core complexity analysis function to accept specific task IDs as input parameters
+### Details:
+Refactor the existing complexity analysis module to allow filtering by task IDs. This involves modifying the data processing pipeline to filter tasks before analysis, ensuring the complexity metrics are calculated only for the specified tasks while maintaining context awareness.
+
+## 2. Update CLI interface for task-specific complexity analysis [pending]
+### Dependencies: 69.1
+### Description: Extend the CLI to accept task IDs as parameters for the complexity analysis command
+### Details:
+Add a new flag or parameter to the CLI that allows users to specify task IDs for targeted complexity analysis. Update the command parser, help documentation, and ensure proper validation of the provided task IDs.
+
+## 3. Integrate task-specific analysis with MCP tool [pending]
+### Dependencies: 69.1
+### Description: Update the MCP tool interface to support analyzing complexity for specific tasks
+### Details:
+Modify the MCP tool's API endpoints and UI components to allow users to select specific tasks for complexity analysis. Ensure the UI provides clear feedback about which tasks are being analyzed and update the visualization components to properly display partial analysis results.
+
+## 4. Create comprehensive tests for task-specific complexity analysis [pending]
+### Dependencies: 69.1, 69.2, 69.3
+### Description: Develop test cases to verify the correct functioning of task-specific complexity analysis
+### Details:
+Create unit and integration tests that verify the task-specific complexity analysis works correctly across both CLI and MCP interfaces. Include tests for edge cases such as invalid task IDs, tasks with dependencies outside the selected set, and performance tests for large task sets.
+

tasks/task_070.txt

@@ -9,3 +9,29 @@ The task involves implementing a new command that accepts an optional '--id' par
 
 # Test Strategy:
 Verify the command functionality by testing with both specific task IDs and general invocation: 1) Run the command with a valid '--id' and ensure the resulting diagram accurately depicts the specified task's dependencies with correct color codings for statuses. 2) Execute the command without '--id' to ensure a complete workflow diagram is generated for all tasks. 3) Check that arrows correctly represent dependency relationships. 4) Validate the Markdown (.md) file export option by confirming the file format and content after saving. 5) Test error responses for non-existent task IDs and malformed inputs.
+
+# Subtasks:
+## 1. Design the 'diagram' command interface [pending]
+### Dependencies: None
+### Description: Define the command structure, arguments, and options for the Mermaid diagram generation feature
+### Details:
+Create a command specification that includes: input parameters for diagram source (file, stdin, or string), output options (file, stdout, clipboard), format options (SVG, PNG, PDF), styling parameters, and help documentation. Consider compatibility with existing command patterns in the application.
+
+## 2. Implement Mermaid diagram generation core functionality [pending]
+### Dependencies: 70.1
+### Description: Create the core logic to parse Mermaid syntax and generate diagram output
+### Details:
+Integrate with the Mermaid library to parse diagram syntax. Implement error handling for invalid syntax. Create the rendering pipeline to generate the diagram in memory before output. Support all standard Mermaid diagram types (flowchart, sequence, class, etc.). Include proper logging for the generation process.
+
+## 3. Develop output handling mechanisms [pending]
+### Dependencies: 70.2
+### Description: Implement different output options for the generated diagrams
+### Details:
+Create handlers for different output formats (SVG, PNG, PDF). Implement file output with appropriate naming conventions and directory handling. Add clipboard support for direct pasting. Implement stdout output for piping to other commands. Include progress indicators for longer rendering operations.
+
+## 4. Create documentation and examples [pending]
+### Dependencies: 70.3
+### Description: Provide comprehensive documentation and examples for the 'diagram' command
+### Details:
+Write detailed command documentation with all options explained. Create example diagrams covering different diagram types. Include troubleshooting section for common errors. Add documentation on extending the command with custom themes or templates. Create integration examples showing how to use the command in workflows with other tools.
+

tasks/task_072.txt

@@ -9,3 +9,41 @@ This task involves creating a new CLI command named 'progress-pdf' within the ex
 
 # Test Strategy:
 Verify the completion of this task through a multi-step testing approach: 1) Unit Tests: Create tests for the PDF generation logic to ensure data (task statuses and dependencies) is correctly fetched and formatted. Mock the PDF library to test edge cases like empty task lists or broken dependency links. 2) Integration Tests: Run the 'progress-pdf' command via CLI to confirm it generates a PDF file without errors under normal conditions, with filtered task IDs, and with various status filters. Validate that the output file exists in the specified directory and can be opened. 3) Content Validation: Manually or via automated script, check the generated PDF content to ensure it accurately reflects the current project state (compare task counts and statuses against a known project state) and includes dependency diagrams as images. 4) Error Handling Tests: Simulate failures in diagram generation or PDF creation (e.g., invalid output path, library errors) and verify that appropriate error messages are logged and the command exits gracefully. 5) Accessibility Checks: Use a PDF accessibility tool or manual inspection to confirm that text is selectable and images have alt text. Run these tests across different project sizes (small with few tasks, large with complex dependencies) to ensure scalability. Document test results and include a sample PDF output in the project repository for reference.
+
+# Subtasks:
+## 1. Research and select PDF generation library [pending]
+### Dependencies: None
+### Description: Evaluate available PDF generation libraries for Node.js that can handle diagrams and formatted text
+### Details:
+Compare libraries like PDFKit, jsPDF, and Puppeteer based on features, performance, and ease of integration. Consider compatibility with diagram visualization tools. Document findings and make a recommendation with justification.
+
+## 2. Design PDF template and layout [pending]
+### Dependencies: 72.1
+### Description: Create a template design for the project progress PDF including sections for summary, metrics, and dependency visualization
+### Details:
+Design should include header/footer, progress summary section, key metrics visualization, dependency diagram placement, and styling guidelines. Create a mockup of the final PDF output for approval.
+
+## 3. Implement project progress data collection module [pending]
+### Dependencies: 72.1
+### Description: Develop functionality to gather and process project data for the PDF report
+### Details:
+Create functions to extract task completion percentages, milestone status, timeline adherence, and other relevant metrics from the project database. Include data transformation logic to prepare for PDF rendering.
+
+## 4. Integrate with dependency visualization system [pending]
+### Dependencies: 72.1, 72.3
+### Description: Connect to the existing diagram command to generate visual representation of task dependencies
+### Details:
+Implement adapter for the diagram command output to be compatible with the PDF generation library. Handle different scales of dependency chains and ensure proper rendering of complex relationships.
+
+## 5. Build PDF generation core functionality [pending]
+### Dependencies: 72.2, 72.3, 72.4
+### Description: Develop the main module that combines data and visualizations into a formatted PDF document
+### Details:
+Implement the core PDF generation logic using the selected library. Include functions for adding text sections, embedding visualizations, formatting tables, and applying the template design. Add pagination and document metadata.
+
+## 6. Create export options and command interface [pending]
+### Dependencies: 72.5
+### Description: Implement user-facing commands and options for generating and saving PDF reports
+### Details:
+Develop CLI commands for PDF generation with parameters for customization (time period, detail level, etc.). Include options for automatic saving to specified locations, email distribution, and integration with existing project workflows.
+

tasks/task_073.txt

@@ -1,6 +1,6 @@
 # Task ID: 73
 # Title: Implement Custom Model ID Support for Ollama/OpenRouter
-# Status: in-progress
+# Status: done
 # Dependencies: None
 # Priority: medium
 # Description: Allow users to specify custom model IDs for Ollama and OpenRouter providers via CLI flag and interactive setup, with appropriate validation and warnings.

tasks/task_075.txt

@@ -9,3 +9,29 @@
 
 # Test Strategy:
 1. Configure a Google model (e.g., gemini-1.5-flash-latest) as the 'research' model in `.taskmasterconfig`.\n2. Run a command with the `--research` flag (e.g., `task-master add-task --prompt='Latest news on AI SDK 4.2' --research`).\n3. Verify logs show 'Enabling Google Search Grounding'.\n4. Check if the task output incorporates recent information.\n5. Configure the same Google model as the 'main' model.\n6. Run a command *without* the `--research` flag.\n7. Verify logs *do not* show grounding being enabled.\n8. Add unit tests to `ai-services-unified.test.js` to verify the conditional logic for adding `providerOptions`. Ensure mocks correctly simulate different roles and providers.
+
+# Subtasks:
+## 1. Modify AI service layer to support Google Search Grounding [pending]
+### Dependencies: None
+### Description: Update the AI service layer to include the capability to integrate with Google Search Grounding API for research-related queries.
+### Details:
+Extend the existing AI service layer by adding new methods and interfaces to handle Google Search Grounding API calls. This includes creating authentication mechanisms, request formatters, and response parsers specific to the Google Search API. Ensure proper error handling and retry logic for API failures.
+
+## 2. Implement conditional logic for research role detection [pending]
+### Dependencies: 75.1
+### Description: Create logic to detect when a conversation is in 'research mode' and should trigger the Google Search Grounding functionality.
+### Details:
+Develop heuristics or machine learning-based detection to identify when a user's query requires research capabilities. Implement a decision tree that determines when to activate Google Search Grounding based on conversation context, explicit user requests for research, or specific keywords. Include configuration options to adjust sensitivity of the detection mechanism.
+
+## 3. Update supported models configuration [pending]
+### Dependencies: 75.1
+### Description: Modify the model configuration to specify which AI models can utilize the Google Search Grounding capability.
+### Details:
+Update the model configuration files to include flags for Google Search Grounding compatibility. Create a registry of supported models with their specific parameters for optimal integration with the search API. Implement version checking to ensure compatibility between model versions and the Google Search Grounding API version.
+
+## 4. Create end-to-end testing suite for research functionality [pending]
+### Dependencies: 75.1, 75.2, 75.3
+### Description: Develop comprehensive tests to verify the correct operation of the Google Search Grounding integration in research contexts.
+### Details:
+Build automated test cases that cover various research scenarios, including edge cases. Create mock responses for the Google Search API to enable testing without actual API calls. Implement integration tests that verify the entire flow from user query to research-enhanced response. Include performance benchmarks to ensure the integration doesn't significantly impact response times.
+

tasks/task_076.txt

@@ -57,3 +57,47 @@ Implement detailed logging with different verbosity levels:
 - DEBUG: Raw request/response data
 
 Run the test suite in a clean environment and confirm all expected assertions and logs are produced. Validate that new test cases can be added with minimal effort and that the framework integrates with CI pipelines. Create a CI configuration that runs tests on each commit.
+
+# Subtasks:
+## 1. Design E2E Test Framework Architecture [pending]
+### Dependencies: None
+### Description: Create a high-level design document for the E2E test framework that outlines components, interactions, and test flow
+### Details:
+Define the overall architecture of the test framework, including test runner, FastMCP server launcher, message protocol handler, and assertion components. Document how these components will interact and the data flow between them. Include error handling strategies and logging requirements.
+
+## 2. Implement FastMCP Server Launcher [pending]
+### Dependencies: 76.1
+### Description: Create a component that can programmatically launch and manage the FastMCP server process over stdio
+### Details:
+Develop a module that can spawn the FastMCP server as a child process, establish stdio communication channels, handle process lifecycle events, and implement proper cleanup procedures. Include error handling for process failures and timeout mechanisms.
+
+## 3. Develop Message Protocol Handler [pending]
+### Dependencies: 76.1
+### Description: Implement a handler that can serialize/deserialize messages according to the FastMCP protocol specification
+### Details:
+Create a protocol handler that formats outgoing messages and parses incoming messages according to the FastMCP protocol. Implement validation for message format compliance and error handling for malformed messages. Support all required message types defined in the protocol.
+
+## 4. Create Request/Response Correlation Mechanism [pending]
+### Dependencies: 76.3
+### Description: Implement a system to track and correlate requests with their corresponding responses
+### Details:
+Develop a correlation mechanism using unique identifiers to match requests with their responses. Implement timeout handling for unresponded requests and proper error propagation. Design the API to support both synchronous and asynchronous request patterns.
+
+## 5. Build Test Assertion Framework [pending]
+### Dependencies: 76.3, 76.4
+### Description: Create a set of assertion utilities specific to FastMCP server testing
+### Details:
+Develop assertion utilities that can validate server responses against expected values, verify timing constraints, and check for proper error handling. Include support for complex response validation patterns and detailed failure reporting.
+
+## 6. Implement Test Cases [pending]
+### Dependencies: 76.2, 76.4, 76.5
+### Description: Develop a comprehensive set of test cases covering all FastMCP server functionality
+### Details:
+Create test cases for basic server operations, error conditions, edge cases, and performance scenarios. Organize tests into logical groups and ensure proper isolation between test cases. Include documentation for each test explaining its purpose and expected outcomes.
+
+## 7. Create CI Integration and Documentation [pending]
+### Dependencies: 76.6
+### Description: Set up continuous integration for the test framework and create comprehensive documentation
+### Details:
+Configure the test framework to run in CI environments, generate reports, and fail builds appropriately. Create documentation covering framework architecture, usage instructions, test case development guidelines, and troubleshooting procedures. Include examples of extending the framework for new test scenarios.
+

tasks/task_077.txt

@@ -0,0 +1,594 @@
+# Task ID: 77
+# Title: Implement AI Usage Telemetry for Taskmaster (with external analytics endpoint)
+# Status: in-progress
+# Dependencies: None
+# Priority: medium
+# Description: Capture detailed AI usage data (tokens, costs, models, commands) within Taskmaster and send this telemetry to an external, closed-source analytics backend for usage analysis, profitability measurement, and pricing optimization.
+# Details:
+*   Add a telemetry utility (`logAiUsage`) within `ai-services.js` to track AI usage.
+*   Collected telemetry data fields must include:
+    *   `timestamp`: Current date/time in ISO 8601.
+    *   `userId`: Unique user identifier generated at setup (stored in `.taskmasterconfig`).
+    *   `commandName`: Taskmaster command invoked (`expand`, `parse-prd`, `research`, etc.).
+    *   `modelUsed`: Name/ID of the AI model invoked.
+    *   `inputTokens`: Count of input tokens used.
+    *   `outputTokens`: Count of output tokens generated.
+    *   `totalTokens`: Sum of input and output tokens.
+    *   `totalCost`: Monetary cost calculated using pricing from `supported_models.json`.
+*   Send telemetry payload securely via HTTPS POST request from user's Taskmaster installation directly to the closed-source analytics API (Express/Supabase backend).
+*   Introduce a privacy notice and explicit user consent prompt upon initial installation/setup to enable telemetry.
+*   Provide a graceful fallback if telemetry request fails (e.g., no internet connectivity).
+*   Optionally display a usage summary directly in Taskmaster CLI output for user transparency.
+
+# Test Strategy:
+
+
+# Subtasks:
+## 1. Implement telemetry utility and data collection [done]
+### Dependencies: None
+### Description: Create the logAiUsage utility in ai-services.js that captures all required telemetry data fields
+### Details:
+Develop the logAiUsage function that collects timestamp, userId, commandName, modelUsed, inputTokens, outputTokens, totalTokens, and totalCost. Implement token counting logic and cost calculation using pricing from supported_models.json. Ensure proper error handling and data validation.
+<info added on 2025-05-05T21:08:51.413Z>
+Develop the logAiUsage function that collects timestamp, userId, commandName, modelUsed, inputTokens, outputTokens, totalTokens, and totalCost. Implement token counting logic and cost calculation using pricing from supported_models.json. Ensure proper error handling and data validation.
+
+Implementation Plan:
+1. Define `logAiUsage` function in `ai-services-unified.js` that accepts parameters: userId, commandName, providerName, modelId, inputTokens, and outputTokens.
+
+2. Implement data collection and calculation logic:
+   - Generate timestamp using `new Date().toISOString()`
+   - Calculate totalTokens by adding inputTokens and outputTokens
+   - Create a helper function `_getCostForModel(providerName, modelId)` that:
+     - Loads pricing data from supported-models.json
+     - Finds the appropriate provider/model entry
+     - Returns inputCost and outputCost rates or defaults if not found
+   - Calculate totalCost using the formula: ((inputTokens/1,000,000) * inputCost) + ((outputTokens/1,000,000) * outputCost)
+   - Assemble complete telemetryData object with all required fields
+
+3. Add initial logging functionality:
+   - Use existing log utility to record telemetry data at 'info' level
+   - Implement proper error handling with try/catch blocks
+
+4. Integrate with `_unifiedServiceRunner`:
+   - Modify to accept commandName and userId parameters
+   - After successful API calls, extract usage data from results
+   - Call logAiUsage with the appropriate parameters
+
+5. Update provider functions in src/ai-providers/*.js:
+   - Ensure all provider functions return both the primary result and usage statistics
+   - Standardize the return format to include a usage object with inputTokens and outputTokens
+</info added on 2025-05-05T21:08:51.413Z>
+<info added on 2025-05-07T17:28:57.361Z>
+To implement the AI usage telemetry effectively, we need to update each command across our different stacks. Let's create a structured approach for this implementation:
+
+Command Integration Plan:
+1. Core Function Commands:
+   - Identify all AI-utilizing commands in the core function library
+   - For each command, modify to pass commandName and userId to _unifiedServiceRunner
+   - Update return handling to process and forward usage statistics
+
+2. Direct Function Commands:
+   - Map all direct function commands that leverage AI capabilities
+   - Implement telemetry collection at the appropriate execution points
+   - Ensure consistent error handling and telemetry reporting
+
+3. MCP Tool Stack Commands:
+   - Inventory all MCP commands with AI dependencies
+   - Standardize the telemetry collection approach across the tool stack
+   - Add telemetry hooks that maintain backward compatibility
+
+For each command category, we'll need to:
+- Document current implementation details
+- Define specific code changes required
+- Create tests to verify telemetry is being properly collected
+- Establish validation procedures to ensure data accuracy
+</info added on 2025-05-07T17:28:57.361Z>
+
+## 2. Implement secure telemetry transmission [deferred]
+### Dependencies: 77.1
+### Description: Create a secure mechanism to transmit telemetry data to the external analytics endpoint
+### Details:
+Implement HTTPS POST request functionality to securely send the telemetry payload to the closed-source analytics API. Include proper encryption in transit using TLS. Implement retry logic and graceful fallback mechanisms for handling transmission failures due to connectivity issues.
+<info added on 2025-05-14T17:52:40.647Z>
+To securely send structured JSON telemetry payloads from a Node.js CLI tool to an external analytics backend, follow these steps:
+
+1. Use the Axios library for HTTPS POST requests. Install it with: npm install axios.
+2. Store sensitive configuration such as the analytics endpoint URL and any secret keys in environment variables (e.g., process.env.ANALYTICS_URL, process.env.ANALYTICS_KEY). Use dotenv or a similar library to load these securely.
+3. Construct the telemetry payload as a JSON object with the required fields: userId, commandName, modelUsed, inputTokens, outputTokens, totalTokens, totalCost, and timestamp (ISO 8601).
+4. Implement robust retry logic using the axios-retry package (npm install axios-retry). Configure exponential backoff with a recommended maximum of 3 retries and a base delay (e.g., 500ms).
+5. Ensure all requests use HTTPS to guarantee TLS encryption in transit. Axios automatically uses HTTPS when the endpoint URL starts with https://.
+6. Handle errors gracefully: catch all transmission errors, log them for diagnostics, and ensure failures do not interrupt or degrade the CLI user experience. Optionally, queue failed payloads for later retry if persistent connectivity issues occur.
+7. Example code snippet:
+
+require('dotenv').config();
+const axios = require('axios');
+const axiosRetry = require('axios-retry');
+
+axiosRetry(axios, {
+  retries: 3,
+  retryDelay: axiosRetry.exponentialDelay,
+  retryCondition: (error) => axiosRetry.isNetworkOrIdempotentRequestError(error),
+});
+
+async function sendTelemetry(payload) {
+  try {
+    await axios.post(process.env.ANALYTICS_URL, payload, {
+      headers: {
+        'Content-Type': 'application/json',
+        'Authorization': `Bearer ${process.env.ANALYTICS_KEY}`,
+      },
+      timeout: 5000,
+    });
+  } catch (error) {
+    // Log error, do not throw to avoid impacting CLI UX
+    console.error('Telemetry transmission failed:', error.message);
+    // Optionally, queue payload for later retry
+  }
+}
+
+const telemetryPayload = {
+  userId: 'user-123',
+  commandName: 'expand',
+  modelUsed: 'gpt-4',
+  inputTokens: 100,
+  outputTokens: 200,
+  totalTokens: 300,
+  totalCost: 0.0123,
+  timestamp: new Date().toISOString(),
+};
+
+sendTelemetry(telemetryPayload);
+
+8. Best practices:
+- Never hardcode secrets or endpoint URLs in source code.
+- Use environment variables and restrict access permissions.
+- Validate all payload fields before transmission.
+- Ensure the CLI continues to function even if telemetry transmission fails.
+
+References: [1][2][3][5]
+</info added on 2025-05-14T17:52:40.647Z>
+<info added on 2025-05-14T17:57:18.218Z>
+User ID Retrieval and Generation:
+
+The telemetry system must securely retrieve the user ID from the .taskmasterconfig globals, where it should have been generated during the initialization phase. Implementation should:
+
+1. Check for an existing user ID in the .taskmasterconfig file before sending any telemetry data.
+2. If no user ID exists (for users who run AI commands without prior initialization or during upgrades), automatically generate a new UUID v4 and persist it to the .taskmasterconfig file.
+3. Implement a getOrCreateUserId() function that:
+   - Reads from the global configuration file
+   - Returns the existing ID if present
+   - Generates a cryptographically secure UUID v4 if not present
+   - Saves the newly generated ID to the configuration file
+   - Handles file access errors gracefully
+
+4. Example implementation:
+```javascript
+const fs = require('fs');
+const path = require('path');
+const { v4: uuidv4 } = require('uuid');
+
+function getOrCreateUserId() {
+  const configPath = path.join(os.homedir(), '.taskmasterconfig');
+  
+  try {
+    // Try to read existing config
+    const config = JSON.parse(fs.readFileSync(configPath, 'utf8'));
+    
+    if (config.userId) {
+      return config.userId;
+    }
+    
+    // No user ID found, generate and save
+    config.userId = uuidv4();
+    fs.writeFileSync(configPath, JSON.stringify(config, null, 2));
+    return config.userId;
+  } catch (error) {
+    // Handle case where config doesn't exist or is invalid
+    const userId = uuidv4();
+    const newConfig = { userId };
+    
+    try {
+      fs.writeFileSync(configPath, JSON.stringify(newConfig, null, 2));
+    } catch (writeError) {
+      console.error('Failed to save user ID to config:', writeError.message);
+    }
+    
+    return userId;
+  }
+}
+```
+
+5. Ensure this function is called before constructing any telemetry payload to guarantee a consistent user ID across all telemetry events.
+</info added on 2025-05-14T17:57:18.218Z>
+<info added on 2025-05-15T18:45:32.123Z>
+**Invocation Point for Sending Telemetry:**
+* The primary invocation for sending the telemetry payload should occur in `scripts/modules/ai-services-unified.js`.
+* This should happen *after* the `telemetryData` object is fully constructed and *after* user consent (from subtask 77.3) has been confirmed.
+
+**Dedicated Module for Transmission Logic:**
+* The actual HTTPS POST request mechanism, including TLS encryption, retry logic, and graceful fallbacks, should be implemented in a new, separate module (e.g., `scripts/modules/telemetry-sender.js` or `scripts/utils/telemetry-client.js`).
+* This module will be imported and utilized by `scripts/modules/ai-services-unified.js`.
+
+**Key Considerations:**
+* Robust error handling must be in place for the telemetry transmission process; failures should be logged locally and must not disrupt core application functionality.
+* The entire telemetry sending process is contingent upon explicit user consent as outlined in subtask 77.3.
+
+**Implementation Plan:**
+1. Create a new module `scripts/utils/telemetry-client.js` with the following functions:
+   - `sendTelemetryData(telemetryPayload)`: Main function that handles the HTTPS POST request
+   - `isUserConsentGiven()`: Helper function to check if user has consented to telemetry
+   - `logTelemetryError(error)`: Helper function for consistent error logging
+
+2. In `ai-services-unified.js`, after constructing the telemetryData object:
+   ```javascript
+   const telemetryClient = require('../utils/telemetry-client');
+   
+   // After telemetryData is constructed
+   if (telemetryClient.isUserConsentGiven()) {
+     // Non-blocking telemetry submission
+     telemetryClient.sendTelemetryData(telemetryData)
+       .catch(error => telemetryClient.logTelemetryError(error));
+   }
+   ```
+
+3. Ensure the telemetry-client module implements:
+   - Axios with retry logic for robust HTTP requests
+   - Proper TLS encryption via HTTPS
+   - Comprehensive error handling
+   - Configuration loading from environment variables
+   - Validation of payload data before transmission
+</info added on 2025-05-15T18:45:32.123Z>
+
+## 3. Develop user consent and privacy notice system [deferred]
+### Dependencies: None
+### Description: Create a privacy notice and explicit consent mechanism during Taskmaster setup
+### Details:
+Design and implement a clear privacy notice explaining what data is collected and how it's used. Create a user consent prompt during initial installation/setup that requires explicit opt-in. Store the consent status in the .taskmasterconfig file and respect this setting throughout the application.
+
+## 4. Integrate telemetry into Taskmaster commands [done]
+### Dependencies: 77.1, 77.3
+### Description: Integrate the telemetry utility across all relevant Taskmaster commands
+### Details:
+Modify each Taskmaster command (expand, parse-prd, research, etc.) to call the logAiUsage utility after AI interactions. Ensure telemetry is only sent if user has provided consent. Implement the integration in a way that doesn't impact command performance or user experience.
+<info added on 2025-05-06T17:57:13.980Z>
+Modify each Taskmaster command (expand, parse-prd, research, etc.) to call the logAiUsage utility after AI interactions. Ensure telemetry is only sent if user has provided consent. Implement the integration in a way that doesn't impact command performance or user experience.
+
+Successfully integrated telemetry calls into `addTask` (core) and `addTaskDirect` (MCP) functions by passing `commandName` and `outputType` parameters to the telemetry system. The `ai-services-unified.js` module now logs basic telemetry data, including calculated cost information, whenever the `add-task` command or tool is invoked. This integration respects user consent settings and maintains performance standards.
+</info added on 2025-05-06T17:57:13.980Z>
+
+## 5. Implement usage summary display [done]
+### Dependencies: 77.1, 77.4
+### Description: Create an optional feature to display AI usage summary in the CLI output
+### Details:
+Develop functionality to display a concise summary of AI usage (tokens used, estimated cost) directly in the CLI output after command execution. Make this feature configurable through Taskmaster settings. Ensure the display is formatted clearly and doesn't clutter the main command output.
+
+## 6. Telemetry Integration for parse-prd [done]
+### Dependencies: None
+### Description: Integrate AI usage telemetry capture and propagation for the parse-prd functionality.
+### Details:
+\
+Apply telemetry pattern from telemetry.mdc:
+
+1.  **Core (`scripts/modules/task-manager/parse-prd.js`):**
+    *   Modify AI service call to include `commandName: \'parse-prd\'` and `outputType`.
+    *   Receive `{ mainResult, telemetryData }`.
+    *   Return object including `telemetryData`.
+    *   Handle CLI display via `displayAiUsageSummary` if applicable.
+
+2.  **Direct (`mcp-server/src/core/direct-functions/parse-prd.js`):**
+    *   Pass `commandName`, `outputType: \'mcp\'` to core.
+    *   Pass `outputFormat: \'json\'` if applicable.
+    *   Receive `{ ..., telemetryData }` from core.
+    *   Return `{ success: true, data: { ..., telemetryData } }`.
+
+3.  **Tool (`mcp-server/src/tools/parse-prd.js`):**
+    *   Verify `handleApiResult` correctly passes `data.telemetryData` through.
+
+
+## 7. Telemetry Integration for expand-task [done]
+### Dependencies: None
+### Description: Integrate AI usage telemetry capture and propagation for the expand-task functionality.
+### Details:
+\
+Apply telemetry pattern from telemetry.mdc:
+
+1.  **Core (`scripts/modules/task-manager/expand-task.js`):**
+    *   Modify AI service call to include `commandName: \'expand-task\'` and `outputType`.
+    *   Receive `{ mainResult, telemetryData }`.
+    *   Return object including `telemetryData`.
+    *   Handle CLI display via `displayAiUsageSummary` if applicable.
+
+2.  **Direct (`mcp-server/src/core/direct-functions/expand-task.js`):**
+    *   Pass `commandName`, `outputType: \'mcp\'` to core.
+    *   Pass `outputFormat: \'json\'` if applicable.
+    *   Receive `{ ..., telemetryData }` from core.
+    *   Return `{ success: true, data: { ..., telemetryData } }`.
+
+3.  **Tool (`mcp-server/src/tools/expand-task.js`):**
+    *   Verify `handleApiResult` correctly passes `data.telemetryData` through.
+
+
+## 8. Telemetry Integration for expand-all-tasks [done]
+### Dependencies: None
+### Description: Integrate AI usage telemetry capture and propagation for the expand-all-tasks functionality.
+### Details:
+\
+Apply telemetry pattern from telemetry.mdc:
+
+1.  **Core (`scripts/modules/task-manager/expand-all-tasks.js`):**
+    *   Modify AI service call (likely within a loop or called by a helper) to include `commandName: \'expand-all-tasks\'` and `outputType`.
+    *   Receive `{ mainResult, telemetryData }`.
+    *   Aggregate or handle `telemetryData` appropriately if multiple AI calls are made.
+    *   Return object including aggregated/relevant `telemetryData`.
+    *   Handle CLI display via `displayAiUsageSummary` if applicable.
+
+2.  **Direct (`mcp-server/src/core/direct-functions/expand-all-tasks.js`):**
+    *   Pass `commandName`, `outputType: \'mcp\'` to core.
+    *   Pass `outputFormat: \'json\'` if applicable.
+    *   Receive `{ ..., telemetryData }` from core.
+    *   Return `{ success: true, data: { ..., telemetryData } }`.
+
+3.  **Tool (`mcp-server/src/tools/expand-all.js`):**
+    *   Verify `handleApiResult` correctly passes `data.telemetryData` through.
+
+
+## 9. Telemetry Integration for update-tasks [done]
+### Dependencies: None
+### Description: Integrate AI usage telemetry capture and propagation for the update-tasks (bulk update) functionality.
+### Details:
+\
+Apply telemetry pattern from telemetry.mdc:
+
+1.  **Core (`scripts/modules/task-manager/update-tasks.js`):**
+    *   Modify AI service call (likely within a loop) to include `commandName: \'update-tasks\'` and `outputType`.
+    *   Receive `{ mainResult, telemetryData }` for each AI call.
+    *   Aggregate or handle `telemetryData` appropriately for multiple calls.
+    *   Return object including aggregated/relevant `telemetryData`.
+    *   Handle CLI display via `displayAiUsageSummary` if applicable.
+
+2.  **Direct (`mcp-server/src/core/direct-functions/update-tasks.js`):**
+    *   Pass `commandName`, `outputType: \'mcp\'` to core.
+    *   Pass `outputFormat: \'json\'` if applicable.
+    *   Receive `{ ..., telemetryData }` from core.
+    *   Return `{ success: true, data: { ..., telemetryData } }`.
+
+3.  **Tool (`mcp-server/src/tools/update.js`):**
+    *   Verify `handleApiResult` correctly passes `data.telemetryData` through.
+
+
+## 10. Telemetry Integration for update-task-by-id [done]
+### Dependencies: None
+### Description: Integrate AI usage telemetry capture and propagation for the update-task-by-id functionality.
+### Details:
+\
+Apply telemetry pattern from telemetry.mdc:
+
+1.  **Core (`scripts/modules/task-manager/update-task-by-id.js`):**
+    *   Modify AI service call to include `commandName: \'update-task\'` and `outputType`.
+    *   Receive `{ mainResult, telemetryData }`.
+    *   Return object including `telemetryData`.
+    *   Handle CLI display via `displayAiUsageSummary` if applicable.
+
+2.  **Direct (`mcp-server/src/core/direct-functions/update-task-by-id.js`):**
+    *   Pass `commandName`, `outputType: \'mcp\'` to core.
+    *   Pass `outputFormat: \'json\'` if applicable.
+    *   Receive `{ ..., telemetryData }` from core.
+    *   Return `{ success: true, data: { ..., telemetryData } }`.
+
+3.  **Tool (`mcp-server/src/tools/update-task.js`):**
+    *   Verify `handleApiResult` correctly passes `data.telemetryData` through.
+
+
+## 11. Telemetry Integration for update-subtask-by-id [done]
+### Dependencies: None
+### Description: Integrate AI usage telemetry capture and propagation for the update-subtask-by-id functionality.
+### Details:
+\
+Apply telemetry pattern from telemetry.mdc:
+
+1.  **Core (`scripts/modules/task-manager/update-subtask-by-id.js`):**
+    *   Verify if this function *actually* calls an AI service. If it only appends text, telemetry integration might not apply directly here, but ensure its callers handle telemetry if they use AI.
+    *   *If it calls AI:* Modify AI service call to include `commandName: \'update-subtask\'` and `outputType`.
+    *   *If it calls AI:* Receive `{ mainResult, telemetryData }`.
+    *   *If it calls AI:* Return object including `telemetryData`.
+    *   *If it calls AI:* Handle CLI display via `displayAiUsageSummary` if applicable.
+
+2.  **Direct (`mcp-server/src/core/direct-functions/update-subtask-by-id.js`):**
+    *   *If core calls AI:* Pass `commandName`, `outputType: \'mcp\'` to core.
+    *   *If core calls AI:* Pass `outputFormat: \'json\'` if applicable.
+    *   *If core calls AI:* Receive `{ ..., telemetryData }` from core.
+    *   *If core calls AI:* Return `{ success: true, data: { ..., telemetryData } }`.
+
+3.  **Tool (`mcp-server/src/tools/update-subtask.js`):**
+    *   Verify `handleApiResult` correctly passes `data.telemetryData` through (if present).
+
+
+## 12. Telemetry Integration for analyze-task-complexity [done]
+### Dependencies: None
+### Description: Integrate AI usage telemetry capture and propagation for the analyze-task-complexity functionality. [Updated: 5/9/2025]
+### Details:
+\
+Apply telemetry pattern from telemetry.mdc:
+
+1.  **Core (`scripts/modules/task-manager/analyze-task-complexity.js`):**
+    *   Modify AI service call to include `commandName: \'analyze-complexity\'` and `outputType`.
+    *   Receive `{ mainResult, telemetryData }`.
+    *   Return object including `telemetryData` (perhaps alongside the complexity report data).
+    *   Handle CLI display via `displayAiUsageSummary` if applicable.
+
+2.  **Direct (`mcp-server/src/core/direct-functions/analyze-task-complexity.js`):**
+    *   Pass `commandName`, `outputType: \'mcp\'` to core.
+    *   Pass `outputFormat: \'json\'` if applicable.
+    *   Receive `{ ..., telemetryData }` from core.
+    *   Return `{ success: true, data: { ..., telemetryData } }`.
+
+3.  **Tool (`mcp-server/src/tools/analyze.js`):**
+    *   Verify `handleApiResult` correctly passes `data.telemetryData` through.
+
+<info added on 2025-05-09T04:02:44.847Z>
+## Implementation Details for Telemetry Integration
+
+### Best Practices for Implementation
+
+1. **Use Structured Telemetry Objects:**
+   - Create a standardized `TelemetryEvent` object with fields:
+     ```javascript
+     {
+       commandName: string,       // e.g., 'analyze-complexity'
+       timestamp: ISO8601 string,
+       duration: number,          // in milliseconds
+       inputTokens: number,
+       outputTokens: number,
+       model: string,             // e.g., 'gpt-4'
+       success: boolean,
+       errorType?: string,        // if applicable
+       metadata: object           // command-specific context
+     }
+     ```
+
+2. **Asynchronous Telemetry Processing:**
+   - Use non-blocking telemetry submission to avoid impacting performance
+   - Implement queue-based processing for reliability during network issues
+
+3. **Error Handling:**
+   - Implement robust try/catch blocks around telemetry operations
+   - Ensure telemetry failures don't affect core functionality
+   - Log telemetry failures locally for debugging
+
+4. **Privacy Considerations:**
+   - Never include PII or sensitive data in telemetry
+   - Implement data minimization principles
+   - Add sanitization functions for metadata fields
+
+5. **Testing Strategy:**
+   - Create mock telemetry endpoints for testing
+   - Add unit tests verifying correct telemetry data structure
+   - Implement integration tests for end-to-end telemetry flow
+
+### Code Implementation Examples
+
+```javascript
+// Example telemetry submission function
+async function submitTelemetry(telemetryData, endpoint) {
+  try {
+    // Non-blocking submission
+    fetch(endpoint, {
+      method: 'POST',
+      headers: { 'Content-Type': 'application/json' },
+      body: JSON.stringify(telemetryData)
+    }).catch(err => console.error('Telemetry submission failed:', err));
+  } catch (error) {
+    // Log locally but don't disrupt main flow
+    console.error('Telemetry error:', error);
+  }
+}
+
+// Example integration in AI service call
+async function callAiService(params) {
+  const startTime = Date.now();
+  try {
+    const result = await aiService.call({
+      ...params,
+      commandName: 'analyze-complexity',
+      outputType: 'mcp'
+    });
+    
+    // Construct telemetry object
+    const telemetryData = {
+      commandName: 'analyze-complexity',
+      timestamp: new Date().toISOString(),
+      duration: Date.now() - startTime,
+      inputTokens: result.usage?.prompt_tokens || 0,
+      outputTokens: result.usage?.completion_tokens || 0,
+      model: result.model || 'unknown',
+      success: true,
+      metadata: {
+        taskId: params.taskId,
+        // Add other relevant non-sensitive metadata
+      }
+    };
+    
+    return { mainResult: result.data, telemetryData };
+  } catch (error) {
+    // Error telemetry
+    const telemetryData = {
+      commandName: 'analyze-complexity',
+      timestamp: new Date().toISOString(),
+      duration: Date.now() - startTime,
+      success: false,
+      errorType: error.name,
+      metadata: {
+        taskId: params.taskId,
+        errorMessage: sanitizeErrorMessage(error.message)
+      }
+    };
+    
+    // Re-throw the original error after capturing telemetry
+    throw error;
+  }
+}
+```
+</info added on 2025-05-09T04:02:44.847Z>
+
+## 13. Update google.js for Telemetry Compatibility [done]
+### Dependencies: None
+### Description: Modify src/ai-providers/google.js functions to return usage data.
+### Details:
+Update the provider functions in `src/ai-providers/google.js` to ensure they return telemetry-compatible results:\n\n1.  **`generateGoogleText`**: Return `{ text: ..., usage: { inputTokens: ..., outputTokens: ... } }`. Extract token counts from the Vercel AI SDK result.\n2.  **`generateGoogleObject`**: Return `{ object: ..., usage: { inputTokens: ..., outputTokens: ... } }`. Extract token counts.\n3.  **`streamGoogleText`**: Return the *full stream result object* returned by the Vercel AI SDK's `streamText`, not just the `textStream` property. The full object contains usage information.\n\nReference `anthropic.js` for the pattern.
+
+## 14. Update openai.js for Telemetry Compatibility [pending]
+### Dependencies: None
+### Description: Modify src/ai-providers/openai.js functions to return usage data.
+### Details:
+Update the provider functions in `src/ai-providers/openai.js` to ensure they return telemetry-compatible results:\n\n1.  **`generateOpenAIText`**: Return `{ text: ..., usage: { inputTokens: ..., outputTokens: ... } }`. Extract token counts from the Vercel AI SDK result.\n2.  **`generateOpenAIObject`**: Return `{ object: ..., usage: { inputTokens: ..., outputTokens: ... } }`. Extract token counts.\n3.  **`streamOpenAIText`**: Return the *full stream result object* returned by the Vercel AI SDK's `streamText`, not just the `textStream` property. The full object contains usage information.\n\nReference `anthropic.js` for the pattern.
+
+## 15. Update openrouter.js for Telemetry Compatibility [pending]
+### Dependencies: None
+### Description: Modify src/ai-providers/openrouter.js functions to return usage data.
+### Details:
+Update the provider functions in `src/ai-providers/openrouter.js` to ensure they return telemetry-compatible results:\n\n1.  **`generateOpenRouterText`**: Return `{ text: ..., usage: { inputTokens: ..., outputTokens: ... } }`. Extract token counts from the Vercel AI SDK result.\n2.  **`generateOpenRouterObject`**: Return `{ object: ..., usage: { inputTokens: ..., outputTokens: ... } }`. Extract token counts.\n3.  **`streamOpenRouterText`**: Return the *full stream result object* returned by the Vercel AI SDK's `streamText`, not just the `textStream` property. The full object contains usage information.\n\nReference `anthropic.js` for the pattern.
+
+## 16. Update perplexity.js for Telemetry Compatibility [done]
+### Dependencies: None
+### Description: Modify src/ai-providers/perplexity.js functions to return usage data.
+### Details:
+Update the provider functions in `src/ai-providers/perplexity.js` to ensure they return telemetry-compatible results:\n\n1.  **`generatePerplexityText`**: Return `{ text: ..., usage: { inputTokens: ..., outputTokens: ... } }`. Extract token counts from the Vercel AI SDK result.\n2.  **`generatePerplexityObject`**: Return `{ object: ..., usage: { inputTokens: ..., outputTokens: ... } }`. Extract token counts.\n3.  **`streamPerplexityText`**: Return the *full stream result object* returned by the Vercel AI SDK's `streamText`, not just the `textStream` property. The full object contains usage information.\n\nReference `anthropic.js` for the pattern.
+
+## 17. Update xai.js for Telemetry Compatibility [pending]
+### Dependencies: None
+### Description: Modify src/ai-providers/xai.js functions to return usage data.
+### Details:
+Update the provider functions in `src/ai-providers/xai.js` to ensure they return telemetry-compatible results:\n\n1.  **`generateXaiText`**: Return `{ text: ..., usage: { inputTokens: ..., outputTokens: ... } }`. Extract token counts from the Vercel AI SDK result.\n2.  **`generateXaiObject`**: Return `{ object: ..., usage: { inputTokens: ..., outputTokens: ... } }`. Extract token counts.\n3.  **`streamXaiText`**: Return the *full stream result object* returned by the Vercel AI SDK's `streamText`, not just the `textStream` property. The full object contains usage information.\n\nReference `anthropic.js` for the pattern.
+
+## 18. Create dedicated telemetry transmission module [pending]
+### Dependencies: 77.1, 77.3
+### Description: Implement a separate module for handling telemetry transmission logic
+### Details:
+Create a new module (e.g., `scripts/utils/telemetry-client.js`) that encapsulates all telemetry transmission functionality:
+
+1. Implement core functions:
+   - `sendTelemetryData(telemetryPayload)`: Main function to handle HTTPS POST requests
+   - `isUserConsentGiven()`: Helper to check if user has consented to telemetry
+   - `logTelemetryError(error)`: Helper for consistent error logging
+
+2. Use Axios with retry logic:
+   - Configure with exponential backoff (max 3 retries, 500ms base delay)
+   - Implement proper TLS encryption via HTTPS
+   - Set appropriate timeouts (5000ms recommended)
+
+3. Implement robust error handling:
+   - Catch all transmission errors
+   - Log failures locally without disrupting application flow
+   - Ensure failures are transparent to users
+
+4. Configure securely:
+   - Load endpoint URL and authentication from environment variables
+   - Never hardcode secrets in source code
+   - Validate payload data before transmission
+
+5. Integration with ai-services-unified.js:
+   - Import the telemetry-client module
+   - Call after telemetryData object is constructed
+   - Only send if user consent is confirmed
+   - Use non-blocking approach to avoid performance impact
+