expertpack

name	expertpack
description	Work with ExpertPacks — structured knowledge packs for AI agents. Obsidian-compatible: every pack is a valid Obsidian vault with Dataview support. Use when: (1) Loading/consuming an ExpertPack as agent context, (2) Creating or hydrating a new ExpertPack from scratch, (3) Configuring RAG for a pack, (4) Validating or fixing a pack with the CLI tools, (5) Opening or authoring a pack in Obsidian. Triggers on: 'expertpack', 'expert pack', 'esoteric knowledge', 'knowledge pack', 'pack hydration', 'validate pack', 'ep-validate', 'ep-doctor', 'obsidian vault', 'obsidian pack'. For EK ratio measurement and quality evals install expertpack-eval. For exporting an OpenClaw agent as an ExpertPack install expertpack-export. For converting an existing Obsidian Vault into an ExpertPack install obsidian-to-expertpack.
metadata	{"openclaw":{"homepage":"https://expertpack.ai"}}

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 repo root into any pack directory, 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 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.

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. See {skill_dir}/references/cli-commands.md for the exact config snippet.

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. Copy .obsidian/ config into the pack root — from the template/ folder in the public ExpertPack repo (github.com/brianhearn/expert-pack). This makes the pack immediately usable in Obsidian with Dataview and Templater pre-configured. See {skill_dir}/references/cli-commands.md for the copy command.
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. Structure each concept file as an atomic-conceptual unit (v4.1): retriever-anchored opening paragraph, body sections, optional ## Frequently Asked, ## Related Terms, ## Related Concepts, and requires: dependencies where needed. Avoid summaries//propositions//per-domain glossary-*.md aggregator directories.
9. Add meta/source-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. See {skill_dir}/references/cli-commands.md for the exact config snippet. 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

The ExpertPack repo (tools/validator/ at github.com/brianhearn/expert-pack) includes local Python scripts for validation and auto-fix. They operate on local pack files only — no network calls, no external dependencies beyond Python stdlib.

• ep-validate.py — compliance validator (manifest, frontmatter, wikilinks, cross-links, file prefixes, orphans, file size, provenance, chunk sidecars). Add --strict to turn the frontmatter contract into a hard gate; must pass with 0 errors before committing.
• ep-doctor.py — auto-fixes common issues. Always run in dry-run mode first (default behavior); only add the apply flag after reviewing proposed changes. Fix categories: links (includes broken-wikilink removal, composite-safe), fm, hash (provenance backfill), prefix.

Recommended workflow: ep-doctor dry-run → ep-doctor apply → ep-validate → commit.

See {skill_dir}/references/cli-commands.md for exact command syntax.

6. Export an OpenClaw Agent as an ExpertPack

Install the companion skill expertpack-export via clawhub — it handles workspace scanning, distillation, and packaging.

7. Emit Typed Answers (TAC)

When retrieval runs in Reconstruct Mode (retrieval_mode: reconstruct, see RFC-003), agents MUST return a Typed Answer Contract (TAC) envelope instead of free prose. TAC forces every claim in an answer to map to a retrieved source fragment, so answers can be machine-verified rather than trusted blindly.

• Copy the prompt contract from templates/TAC-PROMPT.md into the agent's system prompt.
• The envelope schema lives in schemas/registry/typed-answer.schema.json (spec: schemas/registry/typed-answer.spec.yaml).
• Validate output structurally with python tools/tac/validate_tac.py answer.json; the eval claim_verifier.py --tac scores TAC envelopes against the pack.

In standard retrieval mode file-level citations suffice and TAC is optional; in reconstruct mode each source must carry a fragment_id.