Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Developer Tools
Zettel Link
This skill maintains the Note Embeddings for Zettelkasten, to search notes, retrieve notes, and discover connections between notes.
skill openclawclawhub Free
This skill maintains the Note Embeddings for Zettelkasten, to search notes, retrieve notes, and discover connections between notes.
โฌ 0 downloads โ
0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- README.md, SKILL.md, TODO.md, _meta.json, config/config.json, scripts/config.py
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.1.3
Provenance
- Publisher
- hxy9243
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Zettel Link Skill
This skill provides a suite of idempotent Python scripts to embed, search, and link notes in an Obsidian vault using semantic similarity. All scripts live in scripts/ and support multiple embedding providers. The skill should be triggered when the user wants to search notes, retrieve notes, or discover connections between notes. If the search directory is indexed with embeddings, the skill should prompt the user if they want to create new embeddings.
Dependencies
uv 0.10.0+ Python 3.10+ One of the following embedding providers: Ollama with mxbai-embed-large (local, default) OpenAI API with text-embedding-3-small Google Gemini API with text-embedding-004
Overview of Commands
uv run scripts/config.py: Configure the embedding model and other settings. uv run scripts/embed.py: Embed notes and cache to .embeddings/embeddings.json uv run scripts/search.py: Semantic search over embedded notes uv run scripts/link.py: Discover semantic connections, output to .embeddings/links.json
Step 0 โ Setup and Config
If the config/config.json file does not exist, create it: uv run scripts/config.py This creates config/config.json with defaults: { "model": "mxbai-embed-large", "provider": { "name": "ollama", "url": "http://localhost:11434" }, "max_input_length": 8192, "cache_dir": ".embeddings", "default_threshold": 0.65, "top_k": 5, "skip_dirs": [".obsidian", ".trash", ".embeddings", "Spaces", "templates"], "skip_files": ["CLAUDE.md", "Vault.md", "Dashboard.md", "templates.md"] } To use a remote provider: # OpenAI uv run scripts/config.py --provider openai # Gemini uv run scripts/config.py --provider gemini # Custom model uv run scripts/config.py --provider openai --model text-embedding-3-large To adjust tuning parameters: uv run scripts/config.py --top-k 10 --threshold 0.7 --max-input-length 4096
Step 1 โ Create Embeddings
uv run scripts/embed.py --input <directory> This creates <directory>/.embeddings/embeddings.json with the embedding cache. Incremental updates: Only re-embeds files that have been modified since the last run (based on file modification time). Text truncation: Automatically truncates text to max_input_length before embedding. Stale pruning: Removes entries for files that no longer exist. Force re-embed: Use --force to re-embed everything.
Step 2 โ Semantic Search
uv run scripts/search.py --input <directory> --query "<query>" This embeds the query using the configured provider and compares it with all cached embeddings, returning the top_k most similar notes. Results are saved to <directory>/.embeddings/search_results.json.
Step 3 โ Semantic Connection Discovery
uv run scripts/link.py --input <directory> This computes cosine similarity for all note pairs and outputs connections above the default_threshold to <directory>/.embeddings/links.json. The output includes: A flat list of all link pairs with scores A per-note grouping for easy lookup Tuning: Adjust --threshold to widen or narrow the connection discovery.
Cache
Format: JSON with metadata envelope (metadata + data) Location: <directory>/.embeddings/embeddings.json Metadata: Tracks generation timestamp, model, provider, embedding size Invalidation: Based on file modification time (mtime) Force rebuild: Delete the cache file or use --force flag
Agent Instructions
When using this skill: Always run config.py first if config/config.json does not exist. Run embed.py before search.py or link.py โ the cache must exist. For remote providers (openai, gemini), ensure the API key environment variable is set (or provide a local .env file in the skill directory). All scripts are idempotent and safe to re-run.
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
3 Docs2 Config1 Scripts
- SKILL.md
- README.md
- TODO.md
- scripts/config.py
- _meta.json
- config/config.json