# crg

> crg — n24q02m-crg. Use this tool when you need to efficiently review code and resolve complex dependencies across your codebase. It provides semantic search and call-graph resolution capabilities, allowing for token-efficient code reviews. Ideal for use with git, it helps solve problems related to code navigation, dependency tracking, and code understanding.

Canonical page: https://skillsregistry.net/skills/n24q02m-crg  
JSON: https://api.skillsregistry.net/v1/skills/n24q02m-crg

## Description

Knowledge graph for token-efficient code reviews -- semantic search and call-graph resolution across your codebase.

## Trust

- **Trust score (0–1):** 0.44
- **Verification tier:** scanned
- **Last scanned:** 2026-09-28

## Facts

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

## Source

- **Source listing:** [GitHub](https://github.com/n24q02m/crg)

## 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": "n24q02m-crg"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/n24q02m-crg` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/n24q02m-crg/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