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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 an...
将自我改进代理的学习成果转换为结构化的 ExpertPack。迁移 .learnings/ 目录(LEARNINGS.md、ERRORS.md、FEATURE_REQUESTS.md)及...
brianhearn brianhearn 来源
未分类 clawhub v1.0.1 2 版本 99826.4 Key: 无需
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概述

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.

版本历史

共 2 个版本

  • v1.0.1 当前
    2026-05-03 04:47 安全 安全
  • v1.0.0
    2026-03-31 15:51

安全检测

腾讯云安全 (Keen)

安全,无风险
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腾讯云安全 (Sanbu)

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