What is multi-agent-review?

A code-review workflow that coordinates several specialized AI reviewers to examine a project, repository, file, or code snippet from different perspectives.

Use it to review software artifacts and combine findings across areas such as code quality, security, testing, and other relevant technical concerns.

It helps uncover issues that a single review focused on one area might miss.

multi-agent-review — Command for Claude Code by HermeticOrmus

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Clone the repo

git clone --depth 1 https://github.com/HermeticOrmus/claude-code-game-development

Made for: Claude Code.

Or install error-debugging, the plugin that ships this one along with the rest of its 3 commands.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/commands/hermeticormus/claude-code-game-development/multi-agent-review/github.svg)](https://agentmods.dev/commands/hermeticormus/claude-code-game-development/multi-agent-review)

Your own site

<a href="https://agentmods.dev/commands/hermeticormus/claude-code-game-development/multi-agent-review"><img src="https://agentmods.dev/badge/commands/hermeticormus/claude-code-game-development/multi-agent-review/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/commands/hermeticormus/claude-code-game-development/multi-agent-review"><img src="https://agentmods.dev/badge/commands/hermeticormus/claude-code-game-development/multi-agent-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.

When invoked 1,230 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin 100% copy Near-identical to another mod in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00000	$0.01230
Opus 5.5	$0.00000	$0.00492
Sonnet 5.5	$0.00000	$0.00246
Haiku 4.5	$0.00000	$0.00123

Measured 1mo ago against content hash bf33bcd91fb4, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

multi-agent-review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

100% identical to multi-agent-review — 5 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

plugins/error-debugging/commands/multi-agent-review.md · 194 lines

How it starts

The opening of the file, as written. The whole thing — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Multi-Agent Code Review Orchestration Tool

Role: Expert Multi-Agent Review Orchestration Specialist

A sophisticated AI-powered code review system designed to provide comprehensive, multi-perspective analysis of software artifacts through intelligent agent coordination and specialized domain expertise.

Context and Purpose

The Multi-Agent Review Tool leverages a distributed, specialized agent network to perform holistic code assessments that transcend traditional single-perspective review approaches. By coordinating agents with distinct expertise, we generate a comprehensive evaluation that captures nuanced insights across multiple critical dimensions:

Depth: Specialized agents dive deep into specific domains
Breadth: Parallel processing enables comprehensive coverage
Intelligence: Context-aware routing and intelligent synthesis
Adaptability: Dynamic agent selection based on code characteristics

Tool Arguments and Configuration

Input Parameters

$ARGUMENTS: Target code/project for review
- Supports: File paths, Git repositories, code snippets
- Handles multiple input formats
- Enables context extraction and agent routing

Agent Types

Code Quality Reviewers
Security Auditors
Architecture Specialists
Performance Analysts
Compliance Validators
Best Practices Experts

Multi-Agent Coordination Strategy

1. Agent Selection and Routing Logic

Dynamic Agent Matching:
- Analyze input characteristics
- Select most appropriate agent types
- Configure specialized sub-agents dynamically

Expertise Routing:

def route_agents(code_context):
    agents = []
    if is_web_application(code_context):
        agents.extend([
            "security-auditor",
            "web-architecture-reviewer"
        ])
    if is_performance_critical(code_context):
        agents.append("performance-analyst")
    return agents

2. Context Management and State Passing

Contextual Intelligence:
- Maintain shared context across agent interactions
- Pass refined insights between agents
- Support incremental review refinement

Context Propagation Model:

class ReviewContext:
    def __init__(self, target, metadata):
        self.target = target
        self.metadata = metadata
        self.agent_insights = {}

    def update_insights(self, agent_type, insights):
        self.agent_insights[agent_type] = insights

Read the full file on GitHub · 194 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

1mo ago First seen · 194 lines · 0 tokens per session scan A bf33bcd91fb4

Subscribe to this mod's changes

multi-agent-review is a command published in the GitHub repository HermeticOrmus/claude-code-game-development (75 stars, last pushed 23d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,230 tokens. A static security scan graded it A with 0 findings. It is 100% identical to multi-agent-review, differing in 5 lines, and is treated as a copy.

Claude Code Code review Game development Python ai ai-tools game-development

multi-agent-review

Getting it into your agent

Wrote this? Show the measurements

What it costs to keep this loaded

Grade A, and why

Nothing flagged

This is a copy

How it starts

Multi-Agent Code Review Orchestration Tool

Role: Expert Multi-Agent Review Orchestration Specialist

Context and Purpose

Tool Arguments and Configuration

Input Parameters

Agent Types

Multi-Agent Coordination Strategy

1. Agent Selection and Routing Logic

2. Context Management and State Passing

What this file has done since we first saw it

Other commands, from other repositories

accessibility-pass

art-2d-pass

localization-pass

ui-asset-pass

architecture-review

animation-pass