Embeddings
- 1k installs
- 67k repo stars
- Updated August 4, 2026
- ruvnet/ruflo
embeddings is a Ruflo Claude Code skill that adds semantic search, pattern matching, and knowledge retrieval to AI agents using HNSW indexing, sql.js persistence, and hyperbolic vector support.
About
embeddings is a Ruflo skill for vector search and similarity queries inside Claude and agentic-flow workflows. The skill combines sql.js WASM SQLite for cross-platform persistent embedding caches with HNSW indexing that the documentation cites as 150x to 12,500x faster search, plus a Poincare ball hyperbolic model for hierarchical data and vector normalization utilities. Developers reach for embeddings when building semantic search over code or docs, pattern matching across agent memory, or knowledge retrieval pipelines, and the skill reports 75x faster performance with agentic-flow integration compared to naive approaches. Skip embeddings when exact string matching or simple key lookups suffice without semantic understanding.
- HNSW indexing delivers 150x–12,500x faster semantic search
- sql.js WASM backend provides cross-platform SQLite persistence
- Hyperbolic (Poincaré ball) support for hierarchical data
- Four normalization options: L2, L1, min-max, z-score
- Configurable chunking with overlap control and 75x speedup via agentic-flow ONNX integration
Embeddings by the numbers
- 1,045 all-time installs (skills.sh)
- +3 installs in the week ending Aug 5, 2026 (Skillselion tracking)
- Ranked #1,008 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
- Security screen: LOW risk (skills.sh audit)
- Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/ruvnet/ruflo --skill embeddingsAdd your badge
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| Installs | 1k |
|---|---|
| repo stars | ★ 67k |
| Security audit | 3 / 3 scanners passed |
| Last updated | August 4, 2026 |
| Repository | ruvnet/ruflo ↗ |
How do you add semantic search to Claude agent workflows?
Add fast semantic search, pattern matching, and knowledge retrieval to their AI agents and Claude workflows.
Who is it for?
Developers building AI agents that need fast semantic search, pattern matching, or RAG-style knowledge retrieval.
Skip if: Exact text matching, simple database lookups, or projects with no semantic similarity requirements.
When should I use this skill?
A developer asks for vector embeddings, semantic search, HNSW indexing, or similarity queries in an agent workflow.
What you get
HNSW-indexed embedding store with sql.js cache, similarity queries, and hyperbolic hierarchical vectors.
- Embedding index
- Persistent vector cache
- Semantic search queries
By the numbers
- HNSW search cited as 150x-12,500x faster in skill documentation
- 75x faster performance with agentic-flow integration
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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Choose embeddings over generic search skills when agent workflows need persistent vector caches and HNSW-accelerated similarity queries.
FAQ
What indexing does the embeddings skill use?
The embeddings skill uses HNSW indexing for fast approximate nearest-neighbor search, with documentation citing 150x to 12,500x speedups. Persistent caches are stored via sql.js WASM SQLite across platforms.
When should developers skip the embeddings skill?
Developers should skip embeddings when tasks need exact text matching or simple key lookups without semantic understanding. The skill targets similarity queries, pattern matching, and knowledge retrieval in agent workflows.
Is Embeddings safe to install?
skills.sh reports 3 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.