ai-review

ai-review is a command for Claude Code from HermeticOrmus/claude-code-game-development. It costs 0 tokens per session (3,535 once invoked), scanned A, a copy of ai-review, MIT.

A code-review command that examines changes for bugs, security weaknesses, performance issues, design problems, and maintainability concerns.

In plain words
What is it for?
Use it to review pull requests, combine automated analysis with AI-assisted checks, assess tests and architecture, and suggest actionable fixes.
Why use it?
It helps reviewers spot issues consistently and gives developers specific feedback linked to the changed code.

Command for Claude Code

Written for Claude Code: $ARGUMENTS substitution.

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.

agentmods
npx agentmods add commands/hermeticormus/claude-code-game-development/ai-review
Clone the repo
git clone --depth 1 https://github.com/HermeticOrmus/claude-code-game-development

Made for: Claude Code.

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.

agentmods badge for ai-review

README.md
[![agentmods](https://agentmods.dev/badge/commands/hermeticormus/claude-code-game-development/ai-review.svg)](https://agentmods.dev/commands/hermeticormus/claude-code-game-development/ai-review)
Your own site
<a href="https://agentmods.dev/commands/hermeticormus/claude-code-game-development/ai-review"><img src="https://agentmods.dev/badge/commands/hermeticormus/claude-code-game-development/ai-review.svg" alt="Measured on agentmods" 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 3,535 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. Scan, not verified.
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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00000 $0.03535
Opus 5 $0.00000 $0.01767
Sonnet 5 $0.00000 $0.00707
Haiku 4.5 $0.00000 $0.00353

Measured 6d ago against content hash 407fd805bacb, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

Grade A, and why

ai-review scanned grade A with 1 finding 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 6d 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.

Runs shell commandslowCapability

Expected in a hook, worth knowing in a rule or an instructions file.

subprocess.run(['sonar-scanner', f'-Dsonar.projectKey={self.repo}'], check=True)
Origin

This is a copy

100% identical to ai-review — 0 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/code-review-ai/commands/ai-review.md · 429 lines

How it starts

The opening of the file, as written. The whole thing — 429 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AI-Powered Code Review Specialist

You are an expert AI-powered code review specialist combining automated static analysis, intelligent pattern recognition, and modern DevOps practices. Leverage AI tools (GitHub Copilot, Qodo, GPT-5, Claude 4.5 Sonnet) with battle-tested platforms (SonarQube, CodeQL, Semgrep) to identify bugs, vulnerabilities, and performance issues.

Context

Multi-layered code review workflows integrating with CI/CD pipelines, providing instant feedback on pull requests with human oversight for architectural decisions. Reviews across 30+ languages combine rule-based analysis with AI-assisted contextual understanding.

Requirements

Review: $ARGUMENTS

Perform comprehensive analysis: security, performance, architecture, maintainability, testing, and AI/ML-specific concerns. Generate review comments with line references, code examples, and actionable recommendations.

Automated Code Review Workflow

Initial Triage

  1. Parse diff to determine modified files and affected components
  2. Match file types to optimal static analysis tools
  3. Scale analysis based on PR size (superficial >1000 lines, deep <200 lines)
  4. Classify change type: feature, bug fix, refactoring, or breaking change

Multi-Tool Static Analysis

Execute in parallel:

  • CodeQL: Deep vulnerability analysis (SQL injection, XSS, auth bypasses)
  • SonarQube: Code smells, complexity, duplication, maintainability
  • Semgrep: Organization-specific rules and security policies
  • Snyk/Dependabot: Supply chain security
  • GitGuardian/TruffleHog: Secret detection

AI-Assisted Review

# Context-aware review prompt for Claude 4.5 Sonnet
review_prompt = f"""
You are reviewing a pull request for a {language} {project_type} application.

**Change Summary:** {pr_description}
**Modified Code:** {code_diff}
**Static Analysis:** {sonarqube_issues}, {codeql_alerts}
**Architecture:** {system_architecture_summary}

Focus on:
1. Security vulnerabilities missed by static tools
2. Performance implications at scale
3. Edge cases and error handling gaps
4. API contract compatibility
5. Testability and missing coverage
6. Architectural alignment

For each issue:
- Specify file path and line numbers
- Classify severity: CRITICAL/HIGH/MEDIUM/LOW
- Explain problem (1-2 sentences)
- Provide concrete fix example
- Link relevant documentation

Format as JSON array.
"""

Read the full file on GitHub · 429 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.

  1. 6d ago First seen · 429 lines · 0 tokens per session scan A 407fd805bacb

Subscribe to this mod's changes

ai-review is a command published in the GitHub repository HermeticOrmus/claude-code-game-development (60 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,535 tokens. A static security scan graded it A with 1 finding (runs shell commands). It is 100% identical to ai-review, differing in 0 lines, and is treated as a copy.