ClawHub

Self Improving To Expertpack

Convert Self-Improving Agent learnings into a structured ExpertPack. Migrates the .learnings/ directory (LEARNINGS.md, ERRORS.md, FEATURE_REQUESTS.md) and any promoted content from workspace files into ExpertPack's portable format with multi-layer retrieval, context tiers, and EK measurement. Output is Obsidian-compatible — includes YAML frontmatter on all content files and can be opened as an Obsidian vault. Use when: upgrading from Self-Improving Agent to ExpertPack, backing up agent learnings, exporting accumulated knowledge, or migrating to a new platform. Triggers on: 'self-improving to expertpack', 'convert self-improving', 'export learnings', 'migrate self-improving', 'learnings to expertpack', 'convert learnings to pack'.

Audits

Pass
ClawScanPass

Agentic behavior and permission review.

Static analysisPass

Pattern checks against bundled files.

VirusTotalPass

Multi-engine malware detections and file reputation.

Install

openclaw skills install self-improving-to-expertpack

Self-Improving Agent → ExpertPack

Converts a Self-Improving Agent skill's .learnings/ directory (3.8K ClawHub installs) into a properly structured ExpertPack.

Supported sources:

  • LEARNINGS.md — corrections, knowledge gaps, best practices, simplify-and-harden patterns
  • ERRORS.md — command failures, exceptions, integration issues
  • FEATURE_REQUESTS.md — user-requested capabilities and implementation notes
  • Promoted content — entries already promoted to CLAUDE.md, AGENTS.md, SOUL.md, TOOLS.md (detected and cross-referenced)

Usage

cd /root/.openclaw/workspace/ExpertPack/skills/self-improving-to-expertpack
python3 scripts/convert.py \
  --workspace /path/to/your/workspace \
  --output ~/expertpacks/my-learnings-pack \
  [--name "My Agent's Learnings"] \
  [--type auto|person|agent|process]

Override .learnings/ location with --learnings /path/to/.learnings.

What It Produces

A complete ExpertPack conforming to schema 2.3:

  • manifest.yaml (with context tiers, EK stub)
  • overview.md summarizing conversion (entry counts, categories, priority breakdown)
  • Structured directories mapped from learning types:
    • mind/ — best practices, conventions, behavioral patterns, promoted rules
    • facts/ — knowledge gaps filled, project-specific facts
    • operational/ — error resolutions, tool gotchas, integration fixes
    • summaries/ — pattern analyses, recurring issue summaries
    • relationships/ — cross-references between related entries
  • _index.md files, lead summaries, glossary.md (if terms/tags found)
  • relations.yaml (from See Also links and shared tags)
  • Clean deduplication preferring promoted > resolved > pending entries

Secrets are automatically stripped (sk-, ghp_, tokens, passwords). Warnings emitted for any found.

Post-Conversion Steps

  1. cd ~/expertpacks/my-learnings-pack
  2. Verify content files are 400–800 tokens each (Schema 2.5 — retrieval-ready by design)
  3. Measure EK ratio: python3 /path/to/expertpack/tools/eval-ek.py .
  4. Review overview.md and manifest.yaml
  5. Commit to git and publish to ClawHub

Learn more: https://expertpack.ai • ClawHub expertpack skill

See also: Self-Improving Agent skill on ClawHub.