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ExpertPack

作者 Brian Hearn · GitHub ↗ · v1.4.1 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install expertpack
功能描述
Work with ExpertPacks — structured knowledge packs for AI agents. Obsidian-compatible: every pack is a valid Obsidian vault with Dataview support. Use when:...
使用说明 (SKILL.md)

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.

安全使用建议
This skill appears to do what it says and is low-risk as distributed (instruction-only, no credentials). Before using it: (1) Inspect any ExpertPack directories you point the agent at — Tier 1 files are loaded into session context, so remove secrets or private configs from packs; (2) Verify the external links / companion tools (GitHub repo and clawhub packages) before installing those extras; (3) Note the bundle contains an internal _meta.json that disagrees with the registry metadata (version/timestamp mismatch) — confirm you have the intended version from the official homepage/GitHub; (4) Test with non-sensitive sample packs first to validate behavior; (5) If you later install companion CLI/eval/export tools, audit their install sources and permissions separately.
功能分析
Type: OpenClaw Skill Name: expertpack Version: 1.4.1 The expertpack skill bundle provides a framework and instructions for managing structured knowledge packs (ExpertPacks) for AI agents. The bundle consists of documentation and schemas (SKILL.md, schemas.md, hydration.md) that guide the agent in reading, creating, and organizing Markdown-based knowledge vaults. While the instructions involve file system operations and mention fetching data from user-defined 'source' URLs for volatile content, the documentation explicitly states that network actions must be user-initiated and emphasizes anti-hallucination and data integrity. No evidence of malicious intent, data exfiltration, or unauthorized execution was found; the skill is well-aligned with its stated purpose of knowledge management.
能力评估
Purpose & Capability
Name/description (ExpertPacks / Obsidian-compatible knowledge packs) match the runtime instructions: reading manifest/overview, loading tiered content, guiding hydration and RAG configuration. References to companion CLI/eval/export skills and a GitHub repo are appropriate for the stated ecosystem.
Instruction Scope
SKILL.md tells the agent to read local pack files (manifest.yaml, overview.md, Tier 1/2/3 files) and to load Tier 1 files into session context. This is necessary for the skill's function, but means the agent will ingest whatever lives in the pack paths you point it at — so packs must be free of secrets or sensitive files. The docs explicitly state no automatic background network fetches and that volatile-file refreshes are user-initiated, which limits surprise network activity.
Install Mechanism
Instruction-only skill with no install spec and no bundled code. No downloads, binaries, or extract steps — lowest-risk install footprint. It points to optional companion tools (expertpack-cli, expertpack-eval, etc.) which are separate installs; those should be audited separately before installing.
Credentials
No required environment variables, no credentials, and no required config paths declared. The only config interaction described is advising users to add the pack directory to openclaw.json memorySearch.extraPaths — appropriate and proportional to the task.
Persistence & Privilege
always is false and model invocation is allowed (default). The skill does not request persistent system-wide changes or cross-skill config access. It documents helper scripts (e.g., ep-strip-frontmatter) but does not attempt to modify other skills' configs.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install expertpack
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /expertpack 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.4.1
Schema refs updated to v4.1; removed private instance/EZT references; repo URLs updated to brianhearn/expert-pack
v1.3.1
Mention EP MCP (github.com/brianhearn/ep-mcp) as companion tool for serving packs as MCP endpoints
v2.1.0
Split CLI tooling into expertpack-cli companion skill; removed shell exec instructions from core skill to keep it focused on consumption and hydration guidance
v2.0.5
Schema v3.1: add provenance metadata spec (v3.0), graph export spec (v3.1), ep-graph-export.py and ep-strip-frontmatter.py tool references; bump validator check count to 19
v2.0.4
Move CLI/shell commands out of SKILL.md into references/cli-commands.md; SKILL.md is now prose-only
v2.0.3
Fix stale _meta.json version mismatch (was 1.0.0, now 2.0.3)
v2.0.2
Clarify dry-run requirement for ep-doctor --apply; clarify volatile file refresh is always user-initiated (no background network fetches)
v1.8.2
Security scan improvements: clarify validator scripts are local-only (no network calls), add dry-run-first guidance, note template source is public GitHub repo
v1.8.1
- Added support and documentation for the companion skill `obsidian-to-expertpack` (convert Obsidian Vaults to ExpertPacks). - Updated triggers and guidance in the description to reference the new conversion workflow. - No breaking changes to workflows or existing pack structure.
v1.8.0
Add Validate & Fix action: ep-validate.py (16 checks), ep-doctor.py (auto-fixer with links/fm/prefix ops), ep-fix-broken-wikilinks.py. Updated description triggers to include validate/doctor keywords. Tools available in ExpertPack/tools/validator/ (public repo). Schema 2.9: wikilinks required, content-type filename prefixes for vault-wide uniqueness, Obsidian graph traversability.
v1.7.0
Hydration: .obsidian/ config copy added as required step 4 — new packs are Obsidian-ready from the start of hydration, not as an afterthought.
v1.6.0
Security: stripped requires/data_access/external_services from metadata — skill is pure instructions with no scripts, no external calls, no file access. Companion skills (expertpack-eval, expertpack-export) handle all execution.
v1.5.0
Security: removed export scripts (scan/distill/compose/validate) from main skill — they belong in expertpack-export. Main skill now covers consumption, hydration, and RAG only. No external API calls, no workspace scanning.
v1.1.1
Core 2.8: Obsidian compatibility
v1.4.0
Core 2.8: Obsidian compatibility
v2.0.1
Security/transparency: declared data_access and external_services in metadata. Export scripts are local-only (no external calls). Added inline privacy note clarifying what accesses what. EK eval (OpenRouter calls) remains isolated to the expertpack-eval companion skill.
v2.0.0
Schema 2.4–2.7: schema-aware chunking (schema IS the chunker), volatile/ TTL for time-bound EK, removed schema-chunker. Pack-agnostic eval runner. Docs QA sync.
v1.3.0
Removed schema-chunker entirely. Schema 2.5: author files to 400-800 tokens, point RAG at pack root. No external tooling.
v1.2.0
Schema 2.5: Retrieval-ready by design. Schema IS the chunker — no external tool needed for new packs. Updated consumption reference, RAG config (tokens:1000, maxResults:10), three-knob interaction model. Schema-chunker demoted to legacy migration.
v1.1.0
Removed API-calling scripts (eval-ek, run-eval) to resolve VirusTotal suspicious flag. EK measurement and quality evals are now in the companion expertpack-eval skill. Core skill is now purely local file operations.
元数据
Slug expertpack
版本 1.4.1
许可证 MIT-0
累计安装 1
当前安装数 1
历史版本数 21
常见问题

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。

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