# chunksilo

> chunksilo — chetic-chunksilo. Use this tool when you need to perform local semantic searches over your documents to find relevant information based on meaning. It solves problems of information retrieval and document discovery by allowing you to search your documents using natural language queries. You can install it via pip, point it at your documents, and receive search results based on semantic similarity.

Canonical page: https://skillsregistry.net/skills/chetic-chunksilo  
JSON: https://api.skillsregistry.net/v1/skills/chetic-chunksilo

## Description

Local semantic search over your documents via MCP. pip install, point at your docs, search by meaning.

## Trust

- **Trust score (0–1):** 0.12
- **Verification tier:** scanned
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **License:** Apache-2.0
- **Updated:** 2026-09-28

## Source

- **Source listing:** [GitHub](https://github.com/Chetic/chunksilo)

## 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": "chetic-chunksilo"
    }
  }
}
```

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