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:

# 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

# Read current gaps
cat memory/gaps.md

# Check vector memory
python3 scripts/ollama_mem.py stats

# Check cron jobs
openclaw cron list