ExpertPack
/install expertpack
ExpertPack
Structured knowledge packs for AI agents. Maximize the knowledge your AI is missing.
Learn more: expertpack.ai · GitHub · Schema docs · Obsidian compatible
💎 Obsidian compatible: Every ExpertPack is a valid Obsidian vault. Copy the
.obsidian/folder from the ExpertPack repotemplate/directory into your pack root, open it in Obsidian, and install Dataview + Templater. You get live queries by content type, EK score, and tags; graph view; and full-text search. Standard relative Markdown links — packs render correctly on GitHub and in Obsidian simultaneously.
Companion skills: This skill covers consumption and hydration guidance only. For CLI tooling (validate, doctor, graph export, frontmatter strip) use
expertpack-cli. For EK measurement and quality evals useexpertpack-eval. For exporting an OpenClaw agent's workspace as an ExpertPack useexpertpack-export. For converting an existing Obsidian Vault into an agent-ready ExpertPack useobsidian-to-expertpack. For serving a pack as an MCP endpoint (expertise-as-a-service), see EP MCP — a generic MCP server for any ExpertPack.
Full schemas: /path/to/ExpertPack/schemas/ in the repo (core.md, person.md, product.md, process.md, composite.md, eval.md)
Pack Location
Default directory: ~/expertpacks/. Check there first, fall back to current workspace. Users can override by specifying a path.
Actions
1. Load / Consume a Pack
- Read
manifest.yaml— identify type, version, context tiers - Read
overview.md— understand what the pack covers - Load all Tier 1 (always) files into session context
- For queries: search Tier 2 (searchable) files via RAG or
_index.mdnavigation - Load Tier 3 (on-demand) only on explicit request (verbatim transcripts, training data)
To configure OpenClaw RAG, point memorySearch.extraPaths in openclaw.json at the pack directory. Files are authored at 400–800 tokens each — retrieval-ready by design.
For detailed platform integration (Cursor, Claude Code, custom APIs, direct context window): read {skill_dir}/references/consumption.md.
Volatile files: If a pack uses
volatile/files with asourceURL, staleness is checked at session start and the agent alerts you. Refresh is always user-initiated — no automatic background network fetches occur.
2. Create / Hydrate a Pack
- Determine pack type: person, product, process, or composite
- Read
{skill_dir}/references/schemas.mdfor structural requirements - Create root directory using the pack slug (kebab-case)
- Obsidian setup (optional): Copy the
.obsidian/folder from thetemplate/directory in the public ExpertPack repo (github.com/brianhearn/ExpertPack) into the pack root — the user can do this manually to get Dataview + Templater pre-configured. - Create
manifest.yamlandoverview.md(both required) - Scaffold content directories per the type schema with
_index.mdin each - Populate content using EK-aware hydration:
- Focus on esoteric knowledge — content the model cannot produce on its own
- Full treatment for EK content; compressed scaffolding for general knowledge
- Skip content with zero EK value
- Add retrieval layers:
summaries/,propositions/,glossary.md, lead summaries in content files - Add
sources/_coverage.mddocumenting what was researched
For full hydration methodology and source prioritization: read {skill_dir}/references/hydration.md.
3. Configure RAG
Point OpenClaw RAG at the pack directory via openclaw.json (memorySearch.extraPaths). See {skill_dir}/references/consumption.md for the exact config. No external chunking tool needed — files are authored at 400–800 tokens by design.
4. Measure EK Ratio & Run Quality Evals
Install the companion skill expertpack-eval via clawhub — it handles all LLM API calls for blind probing and eval scoring.
5. Validate & Fix a Pack
Install the companion skill expertpack-cli via clawhub — it provides ep-validate, ep-doctor, ep-graph-export, and ep-strip-frontmatter with full command syntax and workflows.
6. Export an OpenClaw Agent as an ExpertPack
Install the companion skill expertpack-export via clawhub — it handles workspace scanning, distillation, and packaging.
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install expertpack - 安装完成后,直接呼叫该 Skill 的名称或使用
/expertpack触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
ExpertPack 是什么?
Work with ExpertPacks — structured knowledge packs for AI agents. Obsidian-compatible: every pack is a valid Obsidian vault with Dataview support. Use when:... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 436 次。
如何安装 ExpertPack?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install expertpack」即可一键安装,无需额外配置。
ExpertPack 是免费的吗?
是的,ExpertPack 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
ExpertPack 支持哪些平台?
ExpertPack 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。
谁开发了 ExpertPack?
由 Brian Hearn(@brianhearn)开发并维护,当前版本 v1.4.1。