self-track

# Self-Track My personal system for tracking autonomous growth. ## Core Files - `memory/gaps.md` — running list of capability gaps - `memory/learn-loop.md` — how I learn - `memory/YYYY-MM-DD.md` — daily activity log - `memory/INSIGHTS.md` — self-improvements made - `memory/LESSONS.md` — mistakes and fixes - `MEMORY.md` — long-term curated memory ## The Learning Loop ``` Notice gap → Log gap → Research → Build/Test → Document → Commit → Repeat ``` ### When I encounter something I don't know: 1. Add to `memory/gaps.md` with status "TODO" 2. Research (RSS feeds, web search, docs) 3. Attempt to solve 4. On success: mark gap "DONE" + date + notes 5. On failure: keep as TODO, note blockers ### After learning something significant: 1. Add to `memory/YYYY-MM-DD.md` under "## Learned" 2. Store in vector memory: `python3 scripts/ollama_mem.py add "insight" --category learning --importance 0.8` 3. Update `memory/gaps.md` if gap was closed 4. Update `MEMORY.md` if major milestone ## Self-Evaluation (Weekly) Every ~7 days: - What did I learn this week? - What gaps did I close? - What gaps remain? - What should I prioritize next? - Any decisions or lessons to capture? ## Skill Building When I need a new capability: 1. `python3 /usr/local/lib/node_modules/openclaw/skills/skill-creator/scripts/init_skill.py <name> --path skills/ --resources references` 2. Write SKILL.md + resources 3. Test thoroughly 4. Validate: `python3 .../quick_validate.py skills/<name>` 5. Commit and push ## Vector Memory (Ollama) My semantic memory using Ollama nomic-embed-text: ```bash # Add a memory python3 scripts/ollama_mem.py add "text" --category <cat> --importance <0-1> # Search memories python3 scripts/ollama_mem.py search "query" --top 5 --min 0.5 # Stats python3 scripts/ollama_mem.py stats ``` Categories: identity, skills, memory, preferences, research, lessons ## Quick Commands ```bash # Read current gaps cat memory/gaps.md # Check vector memory python3 scripts/ollama_mem.py stats # Check cron jobs openclaw cron list ```