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 repo template/ 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 use expertpack-eval. For exporting an OpenClaw agent's workspace as an ExpertPack use expertpack-export. For converting an existing Obsidian Vault into an agent-ready ExpertPack use obsidian-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

1. 1. Read manifest.yaml — identify type, version, context tiers
2. Read overview.md — understand what the pack covers
3. Load all Tier 1 (always) files into session context
4. For queries: search Tier 2 (searchable) files via RAG or _index.md navigation
5. 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 a source URL, 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

1. 1. Determine pack type: person, product, process, or composite
2. Read {skill_dir}/references/schemas.md for structural requirements
3. Create root directory using the pack slug (kebab-case)
4. Obsidian setup (optional): Copy the .obsidian/ folder from the template/ 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.
5. Create manifest.yaml and overview.md (both required)
6. Scaffold content directories per the type schema with _index.md in each
7. Populate content using EK-aware hydration:

1. 8. Add retrieval layers: summaries/, propositions/, glossary.md, lead summaries in content files
2. Add sources/_coverage.md documenting 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.