embeddings
This skill implements HNSW-indexed vector embeddings for similarity queries with SQLite persistence and support for hierarchical data.
Install
mkdir -p .claude/skills/embeddings && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/8764" && unzip -o skill.zip -d .claude/skills/embeddings && rm skill.zipInstalls to .claude/skills/embeddings
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed.Key capabilities
- →Initialize vector embeddings with HNSW indexing
- →Perform semantic search and similarity queries
- →Manage persistent storage via sql.js
- →Apply quantization for memory efficiency
- →Normalize embeddings using L1, L2, or z-score
How it works
It uses HNSW indexing for rapid similarity search and supports persistent storage through sql.js, with optional ONNX integration for performance.
Inputs & outputs
When to use embeddings
- →Building semantic search functionality
- →Performing pattern matching across documents
- →Integrating persistent vector memory in AI agents
- →Optimizing search performance for large databases
About this skill
Embeddings Skill
Purpose
Vector embeddings for semantic search and pattern matching with HNSW indexing.
Features
| Feature | Description |
|---|---|
| sql.js | Cross-platform SQLite persistent cache (WASM) |
| HNSW | 150x-12,500x faster search |
| Hyperbolic | Poincare ball model for hierarchical data |
| Normalization | L2, L1, min-max, z-score |
| Chunking | Configurable overlap and size |
| 75x faster | With agentic-flow ONNX integration |
Commands
Initialize Embeddings
npx claude-flow embeddings init --backend sqlite
Embed Text
npx claude-flow embeddings embed --text "authentication patterns"
Batch Embed
npx claude-flow embeddings batch --file documents.json
Semantic Search
npx claude-flow embeddings search --query "security best practices" --top-k 5
Memory Integration
# Store with embeddings
npx claude-flow memory store --key "pattern-1" --value "description" --embed
# Search with embeddings
npx claude-flow memory search --query "related patterns" --semantic
Quantization
| Type | Memory Reduction | Speed |
|---|---|---|
| Int8 | 3.92x | Fast |
| Int4 | 7.84x | Faster |
| Binary | 32x | Fastest |
Best Practices
- Use HNSW for large pattern databases
- Enable quantization for memory efficiency
- Use hyperbolic for hierarchical relationships
- Normalize embeddings for consistency
When not to use it
- →Exact text matching
- →Simple lookups
- →Tasks not requiring semantic understanding
Prerequisites
Limitations
- →Requires semantic data to be effective
- →Performance depends on chosen quantization and indexing settings
How it compares
It provides a specialized, high-performance vector search workflow compared to generic database queries.
Compared to similar skills
embeddings side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| embeddings (this skill) | 0 | 6mo | Review | Advanced |
| embedding-strategies | 8 | 2mo | No flags | Intermediate |
| pinecone | 3 | 7mo | Review | Intermediate |
| trulens-dataset-curation | 1 | 2mo | Review | Beginner |
Try saying
Example prompts that trigger this skill in your AI assistant.
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