obsidian-to-expertpack

name	obsidian-to-expertpack
description	Convert an existing Obsidian Vault into an agent-ready ExpertPack. Restructures vault content for EK optimization, RAG retrieval, and OpenClaw integration. Creates a copy — source vault is never modified. Use when: a user wants to make their Obsidian Vault usable by AI agents, convert OV to EP, drop their vault into OpenClaw as a knowledge pack, or make their notes RAG-ready. Triggers on: 'obsidian to expertpack', 'obsidian vault to ep', 'convert obsidian', 'OV to EP', 'obsidian agent ready', 'make my vault ai ready', 'obsidian knowledge pack', 'obsidian rag'.
metadata	{"openclaw":{"homepage":"https://expertpack.ai","requires":{"bins":"[Truncated]","pip":"[Truncated]"}}}

Obsidian Vault → ExpertPack

Converts an Obsidian Vault into a structured ExpertPack — agent-ready, RAG-optimized, and OpenClaw-compatible. Source vault is never modified; output is a clean copy.

Learn more: expertpack.ai · GitHub

Companion skills: Install expertpack for full EP workflows. Install expertpack-eval to measure EK ratio after conversion.

Step 1: Analyze the Vault

Before running the script, inspect the vault:

1. List the top-level directories — these map to EP content sections
2. Identify the pack type based on structure:
   • journals/, daily/, people/, mind/ → person
   • concepts/, workflows/, troubleshooting/, faq/ → product
   • phases/, checklists/, decisions/, steps/ → process
   • Mix of the above → composite
3. Note any templates/ or _templates/ folders — exclude from conversion
4. Estimate content volume and identify the highest-EK directories

The script auto-detects type (--type auto) but verify your judgment matches before proceeding. See references/migration-guide.md for the full decision tree.

Step 2: Run the Conversion Script

python3 /path/to/ExpertPack/skills/obsidian-to-expertpack/scripts/convert.py \
  /path/to/obsidian-vault \
  --output ~/expertpacks/my-pack-slug \
  --name "My Pack Name" \
  [--type auto|person|product|process|composite] \
  [--dry-run]

Always do a --dry-run first to preview what will be converted.

What the script produces:

• All .md files copied with EP frontmatter (title, type, tags, pack, created)
• Inline #hashtags extracted into frontmatter tags:
• Dataview query blocks stripped (computed views, not knowledge)
• [text](file.md) links converted to [[wikilinks]]
• manifest.yaml, overview.md, glossary.md at pack root
• _index.md in each content directory
• .obsidian/ config copied (pack opens in Obsidian immediately)

For detailed handling of Obsidian-specific patterns (nested tags, daily notes, templates, attachments): read references/migration-guide.md.

Step 3: Validate & Fix

# Fix common issues first
python3 /path/to/ExpertPack/tools/validator/ep-doctor.py ~/expertpacks/my-pack-slug --apply

# Must reach 0 errors
python3 /path/to/ExpertPack/tools/validator/ep-validate.py ~/expertpacks/my-pack-slug --verbose

# Fix any broken wikilinks (cross-vault references)
python3 /path/to/ExpertPack/tools/validator/ep-fix-broken-wikilinks.py ~/expertpacks/my-pack-slug --apply

Do not proceed until ep-validate reports 0 errors.

Step 4: Agent-Assisted Enhancement

After validation, enhance retrieval quality:

1. Lead summaries — add a 1-3 sentence blockquote at the top of the 5-10 most important files
2. Glossary — populate glossary.md with domain-specific terms (this is Tier 1 — always loaded)
3. Propositions — create propositions/ with atomic factual statements extracted from high-EK files
4. EK triage — identify low-EK files (general knowledge) and compress or remove them
5. File size — split files >3KB on ## header boundaries

Step 5: Configure RAG in OpenClaw

Add to ~/.openclaw/openclaw.json:

{
  "agents": {
    "defaults": {
      "memorySearch": {
        "extraPaths": ["/path/to/your/converted-pack"]
      }
    }
  }
}

Restart OpenClaw after config change. The pack is now searchable in every session.

Step 6: Measure EK Ratio

clawhub install expertpack-eval

Run evals to score how much esoteric knowledge the pack contains vs. what the model already knows. Target EK ratio >0.6 for high-value packs.