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.

1,167
installs: 0 up / 0 down, score 0.00
votes: 78,376
repo stars
Updated: less than a month ago
In the catalog since: 5 Oct 2026
From: ruvnet/ruflo

Get it through your agent npx skills add ruvnet/ruflo@embeddings

Skill instructions

Rendered from the published SKILL.md, 62 lines.

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

Get it through your agent

Call the Graph-of-Skills MCP server's retrieve_skill_bundle tool and it returns this skill's text inside your budget.

{
  "tool": "retrieve_skill_bundle",
  "arguments": {
    "query": "embeddings"
  }
}

New to this? Set up Graph-of-Skills or get a free key. The text on this page comes from its authors: how skills are sourced and what we check. If none fits your task, your agent can write a skill for your own library. To keep a skill from your own library on a machine, see how to install one of your skills.

Share on X Share on LinkedIn Share on Bluesky Share on Hacker News

Share on Reddit

Source

Source repository ruvnet/ruflo
Listing on skills.sh

From ruvnet/ruflo via skills.sh

Related skills

embedding-strategies
Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.
11,421 installs
memory-management
AgentDB memory system with HNSW vector search. Provides 150x-12,500x faster pattern retrieval, persistent storage, and semantic search capabilities for learning and knowledge management. Use when: need to store successful patterns, searching for similar solutions, semantic lookup of past work, learning from previous tasks, sharing knowledge between agents, building knowledge base. Skip when: no learning needed, ephemeral one-off tasks, external data sources available, read-only exploration.
1,190 installs
similarity-search-patterns
Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
10,689 installs
storing-and-querying-vectors
Store and query vector embeddings using Amazon S3 Vectors, a cost-effective long-term vector storage service with its own API namespace (s3vectors). Triggers on: create S3 vector bucket, vector index, store embeddings, semantic search, RAG vector storage, similarity search, vector database, migrate from other vector databases. Do NOT use for: querying tabular data (use querying-data-lake), S3 object storage, or hundreds/thousands of sustained QPS (use OpenSearch).
6,559 installs
rag-retrieval
Retrieval-Augmented Generation patterns for grounded LLM responses. Use when building RAG pipelines, embedding documents, implementing hybrid search, contextual retrieval, HyDE, agentic RAG, multimodal RAG, query decomposition, reranking, or pgvector search.
1,508 installs

More from ruvnet/ruflo

ruflo
Ruflo is a multi-agent orchestration platform for AI coding agents (Claude Code, Cursor, Codex, Copilot, Gemini, Amp, +12 more). Use this skill when the user wants to (1) install/init ruflo in a project, (2) run multi-agent swarms with hierarchical coordination, (3) use ruflo's 314+ MCP tools for memory, routing, hooks, sub-agents, or workflows, (4) check ruflo status/version/doctor health, or (5) discover which of ruflo's 30+ plugins fits their task.
3,957 installs
swarm-orchestration
Multi-agent swarm coordination for complex tasks. Uses hierarchical topology with specialized agents to break down and execute complex work across multiple files and modules. Use when: 3+ files need changes, new feature implementation, cross-module refactoring, API changes with tests, security-related changes, performance optimization across codebase, database schema changes. Skip when: single file edits, simple bug fixes (1-2 lines), documentation updates, configuration changes, quick exploration.
2,116 installs
agent-arch-system-design
Agent skill for arch-system-design - invoke with $agent-arch-system-design
1,424 installs
agent-workflow
Agent skill for workflow - invoke with $agent-workflow
1,409 installs
security-audit
Comprehensive security scanning and vulnerability detection. Includes input validation, path traversal prevention, CVE detection, and secure coding pattern enforcement. Use when: authentication implementation, authorization logic, payment processing, user data handling, API endpoint creation, file upload handling, database queries, external API integration. Skip when: read-only operations on public data, internal development tooling, static documentation, styling changes.
1,398 installs

Content is provided as published by its creators; you are responsible for how you use it.

To request removal, use the contact form.