Learnings Skill

Continuous learning system. Every failure, correction, and pattern gets logged so the same mistakes aren't repeated. Uses its own MeiliSearch index for fast recall.

Security Model

• Credentials: MeiliSearch key is loaded from ~/.openclaw/workspace/.env — never hardcoded in scripts
• Sensitive data filtering: Auto-extraction skips content containing tokens, passwords, API keys, and credentials
• Safe defaults: Distillation and auto-extract default to --dry-run; use --apply to write
• Secure temp files: All temp files use mktemp with chmod 600 and are cleaned up on exit

Capabilities (declared)

• Read: memory/*.md (daily notes, for auto-extraction)
• Write: MeiliSearch learnings index, LEARNINGS.md (distillation only with --apply)
• Execute: curl to localhost MeiliSearch, python3 for document processing

What Gets Logged

Configuration

Requires ~/.openclaw/workspace/.env:

MEILI_HOST=http://127.0.0.1:7700
MEILI_KEY=your-master-key-here

Files

• LEARNINGS.md — Human-readable summary (auto-generated, use --apply to write)
• scripts/setup_index.sh — Create/configure MeiliSearch index
• scripts/log_learning.sh — Log a new learning entry
• scripts/search_learnings.sh — Search past learnings before acting
• scripts/auto_extract.sh — Auto-extract learnings from daily notes
• scripts/distill_learnings.sh — Summarize raw logs into LEARNINGS.md

Usage

# Log a learning
bash scripts/log_learning.sh \
  --category failure \
  --what "deployment fails with auth token expiry" \
  --fix "Refresh token before each deploy" \
  --importance 0.9 \
  --tags "deploy,auth,token"

# Search before acting
bash scripts/search_learnings.sh "github push" 5

# Auto-extract from daily notes (dry-run by default)
bash scripts/auto_extract.sh --days 7 --apply

# Distill into readable summary (dry-run by default)
bash scripts/distill_learnings.sh --apply

How It Integrates

Before taking any action, I search the learnings index:

1. Search for the action/context
2. If a past learning exists, apply the fix first
3. If the action fails, log it immediately
4. If you correct me, log it as a high-priority correction

Version Rules (Learned)

• Increment minor (1.1.0 → 1.2.0) for new features
• Increment major (1.0.0 → 2.0.0) for breaking changes
• Avoid patch-level bumps (1.1.0 → 1.1.1) unless it's an actual bug fix