专家包生成

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 any promoted content from workspace files into ExpertPack's portable format with multi-layer retrieval, context tiers, and EK measurement. 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'.

4.5k效率与工作流未扫描2026年3月23日

安装

claude skill add --url github.com/openclaw/skills/tree/main/skills/brianhearn/self-improving-to-expertpack

必需命令行工具

python3

文档

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

bash
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. Run the ExpertPack chunker: python3 /path/to/expertpack/scripts/chunk.py --pack . --output ./.chunks
  3. Measure EK ratio: python3 /path/to/expertpack/scripts/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.

相关 Skills

技能工坊

by anthropics

Universal
热门

覆盖 Skill 从创建到迭代优化全流程:起草能力、补测试提示、跑评测与基准方差分析,并持续改写内容和描述,提升效果与触发准确率。

技能工坊把技能从创建、迭代到评测串成闭环,方差分析加描述优化,特别适合把触发准确率打磨得更稳。

效率与工作流
未扫描158.9k

PPT处理

by anthropics

Universal
热门

处理 .pptx 全流程:创建演示文稿、提取和解析幻灯片内容、批量修改现有文件,支持模板套用、合并拆分、备注评论与版式调整。

涉及PPTX的创建、解析、修改到合并拆分都能一站搞定,连备注、模板和评论也能处理,做演示文稿特别省心。

效率与工作流
未扫描158.9k

PDF处理

by anthropics

Universal
热门

遇到 PDF 读写、文本表格提取、合并拆分、旋转加水印、表单填写或加解密时直接用它,也能提取图片、生成新 PDF,并把扫描件通过 OCR 变成可搜索文档。

PDF杂活别再来回切工具了,文本表格提取、合并拆分到OCR识别一次搞定,连扫描件也能变可搜索。

效率与工作流
未扫描158.9k

相关 MCP 服务

文件系统

编辑精选

by Anthropic

热门

Filesystem 是 MCP 官方参考服务器,让 LLM 安全读写本地文件系统。

这个服务器解决了让 Claude 直接操作本地文件的痛点,比如自动整理文档或生成代码文件。适合需要自动化文件处理的开发者,但注意它只是参考实现,生产环境需自行加固安全。

效率与工作流
88.1k

by wonderwhy-er

热门

Desktop Commander 是让 AI 直接执行终端命令、管理文件和进程的 MCP 服务器。

这工具解决了 AI 无法直接操作本地环境的痛点,适合需要自动化脚本调试或文件批量处理的开发者。它能让你用自然语言指挥终端,但权限控制需谨慎,毕竟让 AI 执行 rm -rf 可不是闹着玩的。

效率与工作流
6.3k

by stickerdaniel

热门

LinkedIn Profile and Job Scraper 是让 Claude 直接抓取 LinkedIn 个人资料、公司信息和职位详情的工具。

这个服务器解决了招聘和商业调研中手动复制粘贴 LinkedIn 数据的痛点,适合猎头或市场分析师快速获取候选人背景和公司动态。不过,LinkedIn 反爬机制频繁更新,数据稳定性需要持续维护,使用时建议搭配人工验证。

效率与工作流
2.7k

评论