Self Improving To Expertpack
by @brianhearn
Convert Self-Improving Agent learnings into a structured ExpertPack. Migrates the .learnings/ directory (LEARNINGS.md, ERRORS.md, FEATURE_REQUESTS.md) and an...
clawhub install self-improving-to-expertpackπ About This Skill
name: self-improving-to-expertpack description: "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 β 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'." metadata: openclaw: homepage: https://expertpack.ai requires: bins: - python3
Self-Improving Agent β ExpertPack
Converts a Self-Improving Agent skill's .learnings/ directory (3.8K ClawHub installs) into a properly structured ExpertPack.
Supported sources:
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)mind/ β best practices, conventions, behavioral patterns, promoted rules
- facts/ β knowledge gaps filled, project-specific facts
- operational/ β error resolutions, tool gotchas, integration fixes
- summaries/ β pattern analyses, recurring issue summaries
- relationships/ β 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)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 400β800 tokens each (Schema 2.5 β 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 β’ ClawHub expertpack skill
See also: Self-Improving Agent skill on ClawHub.
π‘ Examples
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.