# jutell

> jutell — ju0o-jutell. Use this tool when you need to understand and verify the code generated by AI coding agents like Codex, Claude Code, and OpenCode. It provides a clarify-before and verify-after layer to ensure transparency and accuracy in AI-generated code, solving problems of unclear or incorrect code implementation. By integrating with git, jutell takes in AI-generated code as input and outputs a clear explanation of the code's functionality and potential issues.

Canonical page: https://skillsregistry.net/skills/ju0o-jutell  
JSON: https://api.skillsregistry.net/v1/skills/ju0o-jutell

## Description

Understand what your AI coding agent actually did. A clarify-before / verify-after layer for Codex, Claude Code and OpenCode.

## Trust

- **Trust score (0–1):** 0.50
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **License:** MIT
- **Updated:** 2026-09-21

## Source

- **Source listing:** [GitHub](https://github.com/ju0o/jutell)

## 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": "ju0o-jutell"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/ju0o-jutell` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/ju0o-jutell/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