This commit updates all relevant documentation (READMEs, docs/*, .cursor/rules) to accurately reflect the finalized unified AI service architecture and the new configuration system (.taskmasterconfig + .env/mcp.json). It also includes the final code cleanup steps related to the refactoring.
Key Changes:
1. **Documentation Updates:**
* Revised `README.md`, `README-task-master.md`, `assets/scripts_README.md`, `docs/configuration.md`, and `docs/tutorial.md` to explain the new configuration split (.taskmasterconfig vs .env/mcp.json).
* Updated MCP configuration examples in READMEs and tutorials to only include API keys in the `env` block.
* Added/updated examples for using the `--research` flag in `docs/command-reference.md`, `docs/examples.md`, and `docs/tutorial.md`.
* Updated `.cursor/rules/ai_services.mdc`, `.cursor/rules/architecture.mdc`, `.cursor/rules/dev_workflow.mdc`, `.cursor/rules/mcp.mdc`, `.cursor/rules/taskmaster.mdc`, `.cursor/rules/utilities.mdc`, and `.cursor/rules/new_features.mdc` to align with the new architecture, removing references to old patterns/files.
* Removed internal rule links from user-facing rules (`taskmaster.mdc`, `dev_workflow.mdc`, `self_improve.mdc`).
* Deleted outdated example file `docs/ai-client-utils-example.md`.
2. **Final Code Refactor & Cleanup:**
* Corrected `update-task-by-id.js` by removing the last import from the old `ai-services.js`.
* Refactored `update-subtask-by-id.js` to correctly use the unified service and logger patterns.
* Removed the obsolete export block from `mcp-server/src/core/task-master-core.js`.
* Corrected logger implementation in `update-tasks.js` for CLI context.
* Updated API key mapping in `config-manager.js` and `ai-services-unified.js`.
3. **Configuration Files:**
* Updated API keys in `.cursor/mcp.json`, replacing `GROK_API_KEY` with `XAI_API_KEY`.
* Updated `.env.example` with current API key names.
* Added `azureOpenaiBaseUrl` to `.taskmasterconfig` example.
4. **Task Management:**
* Marked documentation subtask 61.10 as 'done'.
* Includes various other task content/status updates from the diff summary.
5. **Changeset:**
* Added `.changeset/cuddly-zebras-matter.md` for user-facing `expand`/`expand-all` improvements.
This commit concludes the major architectural refactoring (Task 61) and ensures the documentation accurately reflects the current system.
This commit implements several related improvements to the models command and configuration system:
- Added MCP support for the models command:
- Created new direct function implementation in models.js
- Registered modelsDirect in task-master-core.js for proper export
- Added models tool registration in tools/index.js
- Ensured project name replacement when copying .taskmasterconfig in init.js
- Improved .taskmasterconfig copying during project initialization:
- Added copyTemplateFile() call in createProjectStructure()
- Ensured project name is properly replaced in the config
- Restructured tool registration in logical workflow groups:
- Organized registration into 6 functional categories
- Improved command ordering to follow typical workflow
- Added clear group comments for maintainability
- Enhanced documentation in cursor rules:
- Updated dev_workflow.mdc with clearer config management instructions
- Added comprehensive models command reference to taskmaster.mdc
- Clarified CLI vs MCP usage patterns and options
- Added warning against manual .taskmasterconfig editing
BREAKING CHANGE: Taskmaster now requires a `.taskmasterconfig` file for model/parameter settings. Environment variables (except API keys) are no longer used for overrides.
- Throws an error if `.taskmasterconfig` is missing, guiding user to run `task-master models --setup`." -m "- Removed env var checks from config getters in `config-manager.js`." -m "- Updated `env.example` to remove obsolete variables." -m "- Refined missing config file error message in `commands.js`.
Introduces a configurable fallback model and adds support for additional AI provider API keys in the environment setup.
- **Add Fallback Model Configuration (.taskmasterconfig):**
- Implemented a new section in .
- Configured as the default fallback model, enhancing resilience if the primary model fails.
- **Update Default Model Configuration (.taskmasterconfig):**
- Changed the default model to .
- Changed the default model to .
- **Add API Key Examples (assets/env.example):**
- Added example environment variables for:
- (for OpenAI/OpenRouter)
- (for Google Gemini)
- (for XAI Grok)
- Included format comments for clarity.
This commit resolves several issues with the task expansion system to
ensure higher quality subtasks and better synchronization:
1. Task File Generation
- Add automatic regeneration of task files after expanding tasks
- Ensure individual task text files stay in sync with tasks.json
- Avoids manual regeneration steps after task expansion
2. Perplexity API Integration
- Fix 'researchPrompt is not defined' error in Perplexity integration
- Add specialized research-oriented prompt template
- Improve system message for better context and instruction
- Better fallback to Claude when Perplexity unavailable
3. Subtask Parsing Improvements
- Enhance regex pattern to handle more formatting variations
- Implement multiple parsing strategies for different response formats:
* Improved section detection with flexible headings
* Added support for numbered and bulleted lists
* Implemented heuristic-based title and description extraction
- Create more meaningful dummy subtasks with relevant titles and descriptions
instead of generic placeholders
- Ensure minimal descriptions are always provided
4. Quality Verification and Retry System
- Add post-expansion verification to identify low-quality subtask sets
- Detect tasks with too many generic/placeholder subtasks
- Implement interactive retry mechanism with enhanced prompts
- Use adjusted settings for retries (research mode, subtask count)
- Clear existing subtasks before retry to prevent duplicates
- Provide detailed reporting of verification and retry process
These changes significantly improve the quality of generated subtasks
and reduce the need for manual intervention when subtask generation
produces suboptimal results.
