# Embedding Search

> Vector-based semantic search using embeddings. Provides hybrid search combining embeddings with keyword matching, document chunking, and relevance ranking. Inspired by ZeroClaw's memory/embeddings system.

- Skill: `winsorllc/embedding-search` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add winsorllc/embedding-search`
- Raw SKILL.md: https://api.skillmd.com/api/skills/winsorllc/embedding-search/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: winsorllc (https://skillmd.com/u/winsorllc)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/winsorllc/embedding-search

---


# Embedding Search

Vector-based semantic search using simulated embeddings. Provides hybrid search combining keyword matching with semantic similarity.

## Capabilities

- Semantic document search
- Vector similarity matching
- Document chunking and indexing
- Hybrid keyword + vector search
- Relevance scoring and ranking
- Full-text search fallback
- Document categorization
- Configurable similarity thresholds

## When to Use

Use the embedding-search skill when:
- Searching through large document collections
- Need semantic similarity matching
- Building a knowledge base
- Finding related documents
- Implementing RAG (Retrieval Augmented Generation)

## Usage Examples

### Index documents
```bash
node /job/.pi/skills/embedding-search/embed.js index /path/to/documents --output index.json
```

### Search documents
```bash
node /job/.pi/skills/embedding-search/embed.js search "machine learning concepts" --index index.json
```

### Interactive mode
```bash
node /job/.pi/skills/embedding-search/embed.js --interactive --index index.json
```

### Add documents to existing index
```bash
node /job/.pi/skills/embedding-search/embed.js add new-doc.md --index index.json
```

### Hybrid search with weights
```bash
node /job/.pi/skills/embedding-search/embed.js search "cloud deployment" --hybrid --keyword-weight 0.3 --semantic-weight 0.7
```