Embeddings
- 89 installs
- 67k repo stars
- Updated August 4, 2026
- ruvnet/claude-flow
Helps with ai & agent building tasks during AI-assisted development.
About
embeddings is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted coding.
- embeddings
- AI & Agent Building
- AI-coding skill
Embeddings by the numbers
- 89 all-time installs (skills.sh)
- Ranked #4,891 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/ruvnet/claude-flow --skill embeddingsAdd your badge
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| Installs | 89 |
|---|---|
| repo stars | ★ 67k |
| Last updated | August 4, 2026 |
| Repository | ruvnet/claude-flow ↗ |
What it does
Helps with ai & agent building tasks during AI-assisted development.
Files
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 sqliteEmbed Text
npx claude-flow embeddings embed --text "authentication patterns"Batch Embed
npx claude-flow embeddings batch --file documents.jsonSemantic Search
npx claude-flow embeddings search --query "security best practices" --top-k 5Memory 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" --semanticQuantization
| 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
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