Agent Skills: Embeddings 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

1. Use HNSW for large pattern databases
2. Enable quantization for memory efficiency
3. Use hyperbolic for hierarchical relationships
4. Normalize embeddings for consistency