- Removed product owner agent documentation from AGENT_CHANGELOG.md - Kept changelog focused on agent contributions and decisions - Product owner system documentation belongs in separate files - Maintains compact, iterative format as intended Refs: #documentation, #cleanup, #changelog
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DanceLessonsCoach Agent Improvement Log
This file tracks the agent's contributions and decisions. Kept compact and iterative.
🎯 Product Owner Agent System - 2026-04-06
Step 2: Reference Issues in Commit Messages
# Good commit message format
git commit -m "feat: implement optimized workflow (closes #2)"
git commit -m "fix: resolve CI job failure (related to #2)"
git commit -m "docs: update workflow documentation (see #2)"
Step 3: Update Issue Progress
# Add progress comments
gitea-client comment-issue arcodange DanceLessonsCoach 2 "⏳ IN PROGRESS: Implementing workflow optimization"
gitea-client comment-issue arcodange DanceLessonsCoach 2 "✅ COMPLETED: Workflow created and tested"
Step 4: Create New Issues for Discovered Problems
# When you find new issues during work
gitea-client create-issue arcodange DanceLessonsCoach "Issue Title" "Detailed description with steps to reproduce"
Issue Reference Examples in AGENT_CHANGELOG.md
Good Practice:
### 2026-04-06 - Gitea Workflow Optimization
**Issue:** #2
**Commit:** `183933b`
**Message:** `✨ feat: integrate swag fmt and improve CI/CD workflows (closes #2)`
**Changes:**
- Implemented optimized main branch workflow (see #2)
- Added artifact sharing between CI jobs
- Combined version management and Docker build
- Reduced total CI time by 40%
**Related Issue:** https://gitea.arcodange.lab/arcodange/DanceLessonsCoach/issues/2
Discovery Pattern for AI Agents
When starting work, always:
- ✅ Check
gitea-client list-issues arcodange DanceLessonsCoach - ✅ Review AGENT_CHANGELOG.md for recent changes
- ✅ Look for issue references in commit messages
- ✅ Update issue status as you progress
- ✅ Reference issues in all commit messages
Benefits:
- ✅ Clear work tracking and continuity
- ✅ Better collaboration between AI agents
- ✅ Complete audit trail of all changes
- ✅ Easy onboarding for new agents
- ✅ Automatic documentation of progress