37178ff1b9
This commit applies the standard telemetry pattern to the update-subtask command and its corresponding MCP tool.
Key Changes:
1. Core Logic (scripts/modules/task-manager/update-subtask-by-id.js):
- The call to generateTextService now includes commandName: 'update-subtask' and outputType.
- The full response { mainResult, telemetryData } is captured.
- mainResult (the AI-generated text) is used for the appended content.
- If running in CLI mode (outputFormat === 'text'), displayAiUsageSummary is called with the telemetryData.
- The function now returns { updatedSubtask: ..., telemetryData: ... }.
2. Direct Function (mcp-server/src/core/direct-functions/update-subtask-by-id.js):
- The call to the core updateSubtaskById function now passes the necessary context for telemetry (commandName, outputType).
- The successful response object now correctly extracts coreResult.telemetryData and includes it in the data.telemetryData field returned to the MCP client.
145 lines
11 KiB
Plaintext
145 lines
11 KiB
Plaintext
# Task ID: 81
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# Title: Task #81: Implement Comprehensive Local Telemetry System with Future Server Integration Capability
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# Status: pending
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# Dependencies: None
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# Priority: medium
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# Description: Expand the existing telemetry system to capture additional metrics about feature usage, performance, and user behavior patterns, implementing local storage and aggregation of telemetry data with the capability for future server integration.
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# Details:
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This task builds upon the existing telemetry infrastructure (Tasks #77 and #80) to provide more comprehensive insights into how users interact with the application, while storing data locally until a server endpoint becomes available.
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Key implementation details:
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1. Identify and implement additional telemetry data points:
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- Command execution frequency and timing metrics
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- Feature usage patterns (which commands/features are most/least used)
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- Performance metrics (execution time, memory usage, etc.)
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- Error rates and types
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- Session duration and activity patterns
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- System environment information (OS, Node version, etc.)
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2. Implement a local telemetry storage system:
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- Create a robust local storage mechanism to hold telemetry data indefinitely
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- Implement data aggregation to combine similar events and reduce storage size
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- Add data retention policies to prevent excessive local storage usage
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- Implement configurable storage limits and cleanup procedures
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- Design the storage format to be compatible with future server transmission
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3. Add privacy-preserving mechanisms:
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- Ensure all personally identifiable information is properly anonymized
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- Implement data minimization principles (only collect what's necessary)
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- Add user-configurable telemetry levels (basic, enhanced, full)
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- Provide clear documentation on what data is collected and how it's used
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4. Design for future server integration:
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- Create a pluggable transmission architecture that can be connected to a server later
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- Define API contracts and data formats for future server endpoints
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- Add configuration options for server URLs and authentication that will be used later
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- Implement feature flags to easily enable server transmission when available
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5. Add telemetry debugging capabilities:
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- Create a developer mode to view telemetry data being collected
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- Implement logging of telemetry events (when in debug mode)
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- Add commands to export telemetry data for manual analysis
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- Create visualization tools for local telemetry data
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6. Focus on user-facing benefits:
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- Implement personal usage dashboards showing the user's own patterns
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- Add productivity insights based on collected telemetry
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- Create features that allow users to optimize their workflow based on their usage data
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- Ensure all telemetry collection provides immediate value to the user
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# Test Strategy:
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The testing strategy for the expanded telemetry system should be comprehensive and cover all aspects of the implementation:
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1. Unit Tests:
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- Test each telemetry collection function in isolation
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- Verify proper anonymization of sensitive data
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- Test aggregation logic with various input scenarios
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- Validate local storage mechanisms with different data volumes
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- Test data retention and cleanup policies
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2. Integration Tests:
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- Verify telemetry data is properly stored locally
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- Test the complete flow from data collection to local storage
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- Validate that the storage format is suitable for future server transmission
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- Test different application states (startup, shutdown, crash recovery)
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- Verify proper handling of storage failures
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3. End-to-End Tests:
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- Create automated E2E tests that perform various user actions and verify telemetry is captured
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- Test with simulated long-term usage to verify storage efficiency
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- Verify that aggregated data accurately represents the performed actions
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4. Performance Tests:
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- Measure the performance impact of the expanded telemetry system
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- Test with large volumes of telemetry data to ensure efficient handling
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- Verify memory usage remains within acceptable limits
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- Test CPU utilization during telemetry collection and storage operations
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5. Manual Testing:
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- Verify telemetry debug mode correctly displays collected data
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- Test different telemetry level configurations
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- Manually verify the accuracy of collected metrics
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- Test the export functionality and analyze the exported data
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- Validate that user-facing insights and dashboards provide accurate and useful information
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6. Privacy Compliance Testing:
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- Verify no PII is stored without proper anonymization
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- Test opt-out functionality works correctly
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- Ensure telemetry levels properly restrict data collection as configured
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7. Regression Testing:
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- Verify existing functionality continues to work with the expanded telemetry
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- Ensure the system is designed to be compatible with future server integration
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8. User Experience Testing:
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- Test the usability of personal dashboards and insights features
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- Gather feedback on the usefulness of telemetry-based recommendations
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- Verify users can easily understand their own usage patterns
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# Subtasks:
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## 1. Implement Additional Telemetry Data Collection Points [pending]
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### Dependencies: None
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### Description: Extend the telemetry system to capture new metrics including command execution frequency, feature usage patterns, performance metrics, error rates, session data, and system environment information. [Updated: 5/8/2025] [Updated: 5/8/2025] [Updated: 5/8/2025]
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### Details:
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Create new telemetry event types and collection points throughout the codebase. Implement hooks in the command execution pipeline to track timing and frequency. Add performance monitoring for key operations using high-resolution timers. Capture system environment data at startup. Implement error tracking that records error types and frequencies. Add session tracking with start/end events and periodic heartbeats.
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<info added on 2025-05-08T22:57:23.259Z>
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This is a test note added via the MCP tool. The telemetry collection system should be thoroughly tested before implementation.
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</info added on 2025-05-08T22:57:23.259Z>
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<info added on 2025-05-08T22:59:29.818Z>
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For future server integration, Prometheus time-series database with its companion storage solutions (like Cortex or Thanos) would be an excellent choice for handling our telemetry data. The local telemetry collection system should be designed with compatible data structures and metrics formatting that will allow seamless export to Prometheus once server-side infrastructure is in place. This approach would provide powerful querying capabilities, visualization options through Grafana, and scalable long-term storage. Consider implementing the OpenMetrics format locally to ensure compatibility with the Prometheus ecosystem.
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</info added on 2025-05-08T22:59:29.818Z>
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<info added on 2025-05-08T23:02:59.692Z>
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Prometheus would be an excellent choice for server-side telemetry storage and analysis. When designing the local telemetry collection system, we should structure our metrics and events to be compatible with Prometheus' data model (time series with key-value pairs). This would allow for straightforward export to Prometheus once server infrastructure is established. For long-term storage, companion solutions like Cortex or Thanos could extend Prometheus' capabilities, enabling historical analysis and scalable retention. Additionally, adopting the OpenMetrics format locally would ensure seamless integration with the broader Prometheus ecosystem, including visualization through Grafana dashboards.
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</info added on 2025-05-08T23:02:59.692Z>
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## 2. Build Robust Local Telemetry Storage System [pending]
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### Dependencies: None
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### Description: Create a persistent local storage mechanism to hold telemetry data indefinitely with aggregation capabilities to combine similar events and reduce storage requirements.
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### Details:
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Implement a persistent local store using SQLite or similar lightweight database. Create data schemas for different telemetry types. Develop aggregation functions that can combine similar events (e.g., multiple instances of the same command) into summary statistics. Implement data retention policies to prevent excessive storage usage. Add serialization/deserialization for telemetry objects. Design the storage format to be compatible with future server transmission needs.
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## 3. Design Server Transmission Architecture for Future Implementation [pending]
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### Dependencies: None
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### Description: Create a pluggable architecture for future server transmission capabilities while maintaining local-only functionality for now.
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### Details:
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Design a modular transmission system with clear interfaces that can be implemented later when a server becomes available. Define data formats and API contracts for future server endpoints. Add configuration options for server URLs and authentication that will be used in the future. Implement feature flags to easily enable server transmission when available. Create a transmission queue design that can be activated later. Document the architecture for future implementation.
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## 4. Implement Privacy Controls and User Configuration [pending]
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### Dependencies: None
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### Description: Add privacy-preserving mechanisms including data anonymization, minimization principles, and user-configurable telemetry levels.
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### Details:
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Create a telemetry sanitization layer that removes or hashes PII before storage. Implement three telemetry levels (basic, enhanced, full) with clear documentation of what each includes. Add user settings UI for controlling telemetry levels. Create a first-run experience that explains telemetry and requests user consent. Implement runtime filtering of telemetry events based on user settings.
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## 5. Add Telemetry Debugging and Local Analysis Tools [pending]
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### Dependencies: None
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### Description: Create developer tools for debugging telemetry including a developer mode to view collected data, logging capabilities, and local data analysis features.
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### Details:
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Implement a developer console command to toggle telemetry debug mode. Create a UI panel that displays collected telemetry data when in debug mode. Add detailed logging of telemetry events to the application log when debugging is enabled. Create commands to export telemetry data in various formats (JSON, CSV) for manual analysis. Implement basic visualization tools for local telemetry data to help users understand their own usage patterns.
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## 6. Develop User-Facing Telemetry Benefits [pending]
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### Dependencies: 81.1, 81.2
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### Description: Create features that provide immediate value to users based on their telemetry data, focusing on personal insights and workflow optimization.
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### Details:
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Implement a personal usage dashboard that visualizes the user's command usage patterns, feature adoption, and productivity trends. Create a 'productivity insights' feature that offers personalized recommendations based on usage patterns. Add workflow optimization suggestions that help users discover more efficient ways to use the application. Develop weekly/monthly usage reports that users can view to track their own progress. Ensure all telemetry collection has a direct benefit to the user in the absence of server-side analysis.
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