Setup & Installation

Install ExpertPack using the ClawHub CLI or OpenClaw CLI:

clawhub install expertpack

If the CLI is not installed:

npx clawhub@latest install expertpack

Or install with OpenClaw CLI:

openclaw skills install expertpack

View on ClawHub · View on GitHub

What This Skill Does

ExpertPack is a Productivity & Workflow skill for OpenClaw by brianhearn.

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. 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. 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:
    • 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
  8. Add retrieval layers: summaries/, propositions/, glossary.md, lead summaries in content files
  9. 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.

Version History

Latest version: 1.3.1

First published: Mar 16, 2026. Last updated: Apr 14, 2026.

4 versions released.

Frequently Asked Questions

Is ExpertPack free to use?
Yes. ExpertPack is a free, open-source skill available on the OpenClaw Skills Registry. You can install and use it at no cost, and the source code is publicly available for review and contribution.
What platforms does ExpertPack support?
It runs on any platform that supports OpenClaw, including macOS, Linux, and Windows. As long as you have the OpenClaw runtime installed, ExpertPack will work seamlessly across operating systems.
How do I update ExpertPack?
Run openclaw skills update expertpack to get the latest version. OpenClaw will download and apply the update automatically, preserving your existing configuration.
Can I use ExpertPack with other skills?
Yes. OpenClaw skills are composable — you can combine ExpertPack with any other installed skill in your workflows. This allows you to build powerful multi-step automations by chaining skills together.