# Multi-Agent Council Review

> Use this tool when you need to streamline code review processes and improve code quality through collaborative feedback. It solves problems of inefficient manual review and inconsistent feedback by orchestrating multiple LLM agents to provide parallel code review and synthesizing suggestions through a voting mechanism. The tool takes in code submissions and outputs ranked improvement suggestions based on consensus among the LLM agents.

Canonical page: https://skillsregistry.net/skills/dustdustpy-multi-agent-council  
JSON: https://api.skillsregistry.net/v1/skills/dustdustpy-multi-agent-council

## Description

Orchestrates multiple LLM agents to perform parallel code review, then synthesizes and votes on suggestions to rank improvements by consensus.

## Trust

- **Trust score (0–1):** 0.66
- **Verification tier:** scanned
- **Last scanned:** 2026-09-03

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-09-03

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/zonlw6qrsd)
- **Repository:** <https://github.com/dustdustpy/multi-agent-council>

## 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": "dustdustpy-multi-agent-council"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/dustdustpy-multi-agent-council` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/dustdustpy-multi-agent-council/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