worklog

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Update the WORKLOG.md with session progress, architectural decisions, lessons learned, and next steps

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[Project Management]

When & Why to Use This Skill

This Claude skill automates the maintenance of project worklogs by documenting session progress, architectural decisions, and lessons learned. It ensures project continuity and clear communication of technical milestones and future tasks, making it an essential tool for maintaining development momentum and context.

Use Cases

  • Session Handover: Automatically summarizing completed tasks and code changes at the end of a coding session to provide a clear, actionable starting point for the next session or collaborator.
  • Architectural Decision Tracking: Recording the rationale behind significant design choices and technical trade-offs to maintain a historical record of the project's evolution and prevent 'knowledge rot'.
  • Knowledge Retention: Capturing 'Lessons Learned' and non-obvious behaviors encountered during development to avoid repeating mistakes and improve long-term team efficiency.
  • Task Management Integration: Synchronizing git diffs and internal task lists (like 'beans') into a human-readable WORKLOG.md for better project visibility and stakeholder updates.
nameworklog
descriptionUpdate the WORKLOG.md with session progress, architectural decisions, lessons learned, and next steps

Worklog Update Skill

Use this skill at the end of a work session to update the project worklog.

Instructions

  1. Read the current WORKLOG.md to understand the format and recent entries

  2. Review this session's work by examining:

    • Git diff to see what changed
    • Any beans tasks that were completed
    • Conversations and decisions made during the session

  3. Create a new entry at the top of the log (after the header) with today's date and a brief title describing the session's focus

  4. Fill in the sections:

    Achieved:

    • List concrete accomplishments
    • Reference specific files created or modified
    • Note any tasks completed

    Architectural Decisions: (if any were made)

    • DECISION: State what was decided
    • REASON: Explain the rationale
    • Only include significant design choices, not routine implementation details

    Lessons Learned: (if any)

    • Important insights discovered during the session
    • Clarifications from user conversations
    • Gotchas, edge cases, or non-obvious behaviors encountered
    • Things that should be remembered for future sessions
    • Corrections to earlier assumptions

    Next:

    • List immediate next steps for the following session
    • Be specific and actionable

  5. Update beans tasks:

    • Run beans done <id> for completed tasks
    • Run beans add "..." for new tasks identified
    • Run beans list to verify current state

  6. Commit the changes with message: "docs: update worklog for [date]"

Example Entry

## [2025-01-15] WebSocket Audio Pipeline

**Achieved:**
- Implemented bidirectional WebSocket in `backend/src/honeysuckle/main.py`
- Created audio buffer management in `backend/src/honeysuckle/receptionist/audio.py`
- Added basic VAD detection hook

**Architectural Decisions:**
- DECISION: Use raw websockets library instead of FastAPI WebSocket
- REASON: Need lower-level control for audio streaming and buffer management

**Lessons Learned:**
- OpenAI Realtime API expects PCM 16-bit audio at 24kHz, not 16kHz like whisper
- The `response.cancel` event must be sent before clearing local buffers, otherwise audio continues playing
- User clarified: Bluetooth car mics are actually higher quality than expected, don't over-engineer noise handling

**Next:**
- Connect to OpenAI Realtime API
- Implement audio encoding/decoding (PCM 16-bit, 24kHz)
- Add connection health monitoring