Commit cd8892a
feat: Add feedback command and lessons.md integration for continuous improvement
This PR introduces a comprehensive feedback loop system that captures lessons
from PR reviews and applies them to future development work.
## New Features
### 1. /speckit.feedback Command
- Extracts lessons learned from PR review comments and discussions
- Stores lessons in .specify/memory/lessons.md (alongside constitution.md)
- Stores PR-specific summaries in .specify/memory/feedback/pr-<number>-lessons.md
- Supports three modes:
- Auto-detect current PR: --find-current
- Specific PR: --pr <number>
- Manual lesson entry: inline text input
- Categorizes lessons: code-quality, architecture, testing, documentation,
security, performance, other
- Deduplicates lessons and tracks frequency
- Optional auto-commit with --commit flag
### 2. Lessons.md Integration Across Commands
All core speckit commands now load and apply relevant lessons:
- /speckit.specify loads architecture & documentation lessons
- /speckit.plan loads architecture, testing & performance lessons
- /speckit.implement loads code-quality, security & testing lessons
### 3. Supporting Infrastructure
- fetch-pr-feedback.sh script: Fetches PR data using GitHub CLI
- Command templates for all AI agents (Claude, Gemini, Cursor, Copilot, etc.)
- Lessons stored in .specify/memory/ alongside project constitution
- PR-specific feedback preserved in .specify/memory/feedback/
## Files Added
- scripts/bash/fetch-pr-feedback.sh
- templates/commands/feedback.md
## Files Modified
- templates/commands/specify.md (added Step 0: Load Lessons - 6 lines)
- templates/commands/plan.md (added Step 0: Load Lessons - 6 lines)
- templates/commands/implement.md (added Step 0: Load Lessons - 6 lines)
- README.md (added Lessons Learned section explaining the system)
- src/specify_cli/__init__.py (added feedback to next steps output)
## Benefits
- Continuous Improvement: Lessons from PR reviews automatically inform future work
- Universal Compatibility: Works with all 15+ supported AI agents
- Non-Breaking: Optional integration - only activates when lessons.md exists
- Category-Aware: Each command loads only relevant lessons to reduce noise
- Team Learning: Captures institutional knowledge from code reviews
- Compounding Knowledge: Team wisdom accumulates in memory alongside constitution
## How It Works
1. After PR review, run /speckit.feedback to extract lessons
2. Lessons are stored in .specify/memory/lessons.md with categories
3. PR-specific context preserved in .specify/memory/feedback/
4. Future runs of /speckit.specify, /speckit.plan, /speckit.implement
automatically load and apply relevant lessons
5. Teams avoid repeating past mistakes and compound knowledge over time
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>1 parent 9111699 commit cd8892a
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- scripts/bash
- src/specify_cli
- templates/commands
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