self-improving-to-expertpack

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 — 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":"[Truncated]"}}}

Permission Setup Review

python scripts/_init_dirs.py is an optional setup helper for the Self-Improving Agent → ExpertPack skill. It may apply broad filesystem permissions for compatibility. Show the target paths and permission mode, explain the risk, and prefer a narrower mode when shared write access is not needed.

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.