# embgrep

> embgrep — quartzunit-embgrep. Use this tool when you need to perform local semantic searches over files using natural language queries, enabling efficient directory indexing without relying on external services. It solves problems related to file discovery and information retrieval, allowing users to find specific files based on their content. The tool takes in files and natural language queries as input and outputs relevant search results.

Canonical page: https://skillsregistry.net/skills/quartzunit-embgrep  
JSON: https://api.skillsregistry.net/v1/skills/quartzunit-embgrep

## Description

Provides local semantic search over files using embeddings, enabling directory indexing and natural language queries without external services.

## Trust

- **Trust score (0–1):** 0.68
- **Verification tier:** scanned
- **Last scanned:** 2026-08-29

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-08-29

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/h7qknw1vuh)
- **Repository:** <https://github.com/QuartzUnit/embgrep>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "quartzunit-embgrep"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/quartzunit-embgrep` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/quartzunit-embgrep/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt