# bubo

> bubo — mountainowl-bubo. Use this tool when you need to streamline code review processes for GitLab MRs and GitHub PRs, as it provides automated analysis using a chosen Large Language Model (LLM) to identify and post actionable findings as inline review threads. This solves problems of manual code review inefficiencies and improves code quality by catching issues early. It takes Git repository data as input and outputs inline review comments with actionable suggestions.

Canonical page: https://skillsregistry.net/skills/mountainowl-bubo  
JSON: https://api.skillsregistry.net/v1/skills/mountainowl-bubo

## Description

Agentic AI code review for GitLab MRs and GitHub PRs, with the LLM of your choice. Posts only actionable findings as inline review threads.

## 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:** 2026-09-24

## Source

- **Source listing:** [GitHub](https://github.com/mountainowl/bubo)

## 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": "mountainowl-bubo"
    }
  }
}
```

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