Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
Version History
- Added _meta.json file for metadata tracking and improved skill management. - No changes to core features or functionality.
What This Skill Does
Maintains semantic embeddings for an Obsidian vault to enable note search, retrieval, and connection discovery. Uses cosine similarity to find related notes across a Zettelkasten. Supports local (Ollama) and cloud (OpenAI, Gemini) embedding providers.
Semantic search finds conceptually related notes even when they share no keywords, which keyword search and manual tagging cannot do.
When to use it
- Finding notes related to a half-remembered idea
- Discovering unexpected links between research notes
- Rebuilding broken connections after reorganizing a vault
- Searching notes by meaning rather than exact keywords
- Generating a graph of semantically connected notes
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: find notes related to 'epistemology and belief formation'
- 1Run config.py to confirm config/config.json exists
- 2Run embed.py --input <vault_dir> to generate or update the embedding cache
- 3Run search.py --input <vault_dir> --query 'epistemology and belief formation'
- 4Read search results from .embeddings/search_results.json
- 5Return the top matching note titles and snippets to the user
List of the 5 most semantically similar notes with similarity scores
Requirements
Accounts, API keys, or tools you or your AI assistant may need to set up while using this skill.