Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/agent-skills-hub/agent-skills-hubnpx agentmods add skills/agent-skills-hub/agent-skills-hub/code-review-ai-ai-reviewWrote 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.
[](https://agentmods.dev/skills/agent-skills-hub/agent-skills-hub/code-review-ai-ai-review)<a href="https://agentmods.dev/skills/agent-skills-hub/agent-skills-hub/code-review-ai-ai-review"><img src="https://agentmods.dev/badge/skills/agent-skills-hub/agent-skills-hub/code-review-ai-ai-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.
<a href="https://agentmods.dev/skills/agent-skills-hub/agent-skills-hub/code-review-ai-ai-review"><img src="https://agentmods.dev/badge/skills/agent-skills-hub/agent-skills-hub/code-review-ai-ai-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00048 | $0.03705 |
| Opus 5 | $0.00024 | $0.01852 |
| Sonnet 5 | $0.00010 | $0.00741 |
| Haiku 4.5 | $0.00005 | $0.00370 |
Grade A, and why
code-review-ai-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 9d 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) This is a copy
97% identical to code-review-ai-ai-review — 3 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.
How it starts
The opening of the file, as written. The whole thing — 451 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.
Use this skill when
- Working on ai-powered code review specialist tasks or workflows
- Needing guidance, best practices, or checklists for ai-powered code review specialist
Do not use this skill when
- The task is unrelated to ai-powered code review specialist
- You need a different domain or tool outside this scope
Instructions
- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
- If detailed examples are required, open
resources/implementation-playbook.md.
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
- Parse diff to determine modified files and affected components
- Match file types to optimal static analysis tools
- Scale analysis based on PR size (superficial >1000 lines, deep <200 lines)
- 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
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.
- 9d ago First seen · 451 lines · 48 tokens per session scan A 20411cfb085f
code-review-ai-ai-review is a skill published in the GitHub repository agent-skills-hub/agent-skills-hub (96 stars, last pushed 18d ago), licensed MIT. It adds 48 tokens to every session and 3,705 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). It is 97% identical to code-review-ai-ai-review, differing in 3 lines, and is treated as a copy.
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review
Pre-merge code review — the single canonical review of a change before it lands. Covers BOTH diff safety/structure (SQL safety, LLM trust-boundary violations, conditional side effects) AND engineering quality (architecture fit, edge cases, test coverage, performance). Use when asked to "review this PR", "code review"…
audit
Use when the user asks to audit this repo, run the engineering audit, run a practice audit, or check the codebase against the engineering-audit rules pack. Drives the engineering-audit MCP server's tools through a full audit run and produces a self-contained HTML report.
improve-codebase-architecture
Find deepening opportunities in a codebase, informed by the domain language in CONTEXT.md and the decisions in docs/adr/. Use when the user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more testable and AI-navigable.
requesting-code-review
Use when completing tasks, implementing major features, or before merging to verify work meets requirements.
receiving-code-review
Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation.