Can I use this for security audits?

Yes, it features a dedicated security agent focusing on the OWASP Top 10, hardcoded secrets, and injection vulnerabilities, providing a weighted security clearance report.

What is Mentorship Mode?

Mentorship Mode adjusts the feedback tone to be educational and supportive, explaining the 'why' behind every suggestion to help developers learn best practices.

Does it support CI/CD integration?

Yes, the skill includes pre-configured patterns for GitHub Actions and quality gate configurations to block merges based on critical issue counts.

How does the consensus mechanism work?

It uses an LLM-as-judge approach where findings are prioritized only when two or more AI models (such as Claude and Gemini) agree on the specific issue, significantly reducing false positives.

Which AI models are supported?

While Claude is the primary orchestrator, the skill is designed to integrate with Gemini and Codex CLI tools to provide multi-perspective verification.

Multi-AI Code Review

Name: Multi-AI Code Review
Author: adaptationio

byadaptationio

Seguridad y Pruebas

Automates multi-perspective code reviews by orchestrating Claude, Gemini, and Codex to perform deep analysis across five critical dimensions.

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Multi-AI Code Review is a powerful ensemble-based auditing tool that leverages multiple AI models as specialized agents to provide high-confidence, 360-degree evaluations of code changes. By analyzing security, performance, maintainability, correctness, and style simultaneously, it filters out false positives through a consensus-based 'LLM-as-judge' mechanism. This skill is ideal for engineering teams looking to establish rigorous quality gates, automate thorough security audits, and provide educational mentorship feedback directly within their CI/CD or local development workflow.

Características Principales

Specialized mentorship mode for educational developer feedback
LLM-as-judge consensus mechanism to minimize false positives
5-dimensional analysis (Security, Performance, Maintainability, Correctness, Style)
Comprehensive quality scoring and severity-based issue prioritization
Multi-agent ensemble architecture using Claude, Gemini, and Codex
0 GitHub stars

Casos de Uso

Establishing consistent code standards and technical debt assessments
Conducting deep-dive security and performance audits before production releases
Automating high-confidence pull request reviews in CI/CD pipelines

Acerca de

Características Principales

Specialized mentorship mode for educational developer feedback
LLM-as-judge consensus mechanism to minimize false positives
5-dimensional analysis (Security, Performance, Maintainability, Correctness, Style)
Comprehensive quality scoring and severity-based issue prioritization
Multi-agent ensemble architecture using Claude, Gemini, and Codex
0 GitHub stars

Casos de Uso

Establishing consistent code standards and technical debt assessments
Conducting deep-dive security and performance audits before production releases
Automating high-confidence pull request reviews in CI/CD pipelines