How do I install the Embeddings skill?

Run npx skillmds@latest add ruvnet/embeddings in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Embeddings safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: makes network calls. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Embeddings?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Embeddings free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Embeddings?

ruvnet (@ruvnet) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Embeddings by ruvnet — Agent Skill

Name: Embeddings
Rating: 4.2 (5 reviews)
Author: ruvnet

Embeddings

> 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.

ruvnet 67043c2 1.9 KB Updated Sep 9, 2026

File contents

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

Source: ruvnet/ruflo → .agents/skills/embeddings/SKILL.md

---name: embeddingsdescription: > 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.--- # 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 ```bash npx claude-flow embeddings init --backend sqlite ``` ### Embed Text ```bash npx claude-flow embeddings embed --text "authentication patterns" ``` ### Batch Embed ```bash npx claude-flow embeddings batch --file documents.json ``` ### Semantic Search ```bash npx claude-flow embeddings search --query "security best practices" --top-k 5 ``` ## Memory Integration ```bash # 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 --- **Source:** [`ruvnet/ruflo`](https://github.com/ruvnet/ruflo) → `.agents/skills/embeddings/SKILL.md`

Frequently asked questions