- Implement secure telemetry submission service
- Created scripts/modules/telemetry-submission.js with submitTelemetryData function
- Implemented secure filtering: removes commandArgs and fullOutput before remote submission
- Added comprehensive validation using Zod schema for telemetry data integrity
- Implemented exponential backoff retry logic (3 attempts max) with smart retry decisions
- Added graceful error handling that never blocks execution
- Respects user opt-out preferences via config.telemetryEnabled
- Configured for localhost testing endpoint (http://localhost:4444/api/v1/telemetry) for now
- Added comprehensive test coverage with 6/6 passing tests covering all scenarios
- Includes submitTelemetryDataAsync for fire-and-forget submissions
- Implement secure telemetry capture with filtering - Enhanced ai-services-unified.js to capture commandArgs and fullOutput in telemetry - Added filterSensitiveTelemetryData() function to prevent sensitive data exposure - Updated processMCPResponseData() to filter telemetry before sending to MCP clients - Verified CLI displayAiUsageSummary() only shows safe fields - Added comprehensive test coverage with 4 passing tests - Resolved critical security issue: API keys and sensitive data now filtered from responses
This commit significantly improves the functionality by implementing
fuzzy semantic search to find contextually relevant dependencies:
- Add Fuse.js for powerful fuzzy search capability with weighted multi-field matching
- Implement score-based relevance ranking with high/medium relevance tiers
- Enhance context generation to include detailed information about similar tasks
- Fix context shadowing issue that prevented detailed task information from
reaching the AI model
- Add informative CLI output showing semantic search results and dependency patterns
- Improve formatting of dependency information in prompts with task titles
The result is that newly created tasks are automatically placed within the correct
dependency structure without manual intervention, with the AI having much better
context about which tasks are most relevant to the new one being created.
This significantly improves the user experience by reducing the need to manually
update task dependencies after creation, all without increasing token usage or costs.
