Can this skill be used in CI/CD pipelines?

Yes, it is designed to integrate with tools like GitHub Actions to provide automated feedback on pull requests and enforce quality gates.

How does it reduce false positives in code reviews?

It employs an 'LLM-as-judge' pattern, typically only flagging issues as critical or high severity when at least two independent AI models agree on the finding.

Does it provide actionable fixes?

Yes, every finding follows a 'What/Where/Why/How' format, providing specific code snippets and explanations for how to resolve the identified issue.

Which AI models does this skill use?

The skill uses Claude as the primary orchestrator and can integrate with Gemini and Codex as specialized agents for cross-verification and deep reasoning.

What are the five dimensions of analysis?

Every review evaluates code based on Security (OWASP), Performance (latency/complexity), Maintainability (patterns/DRY), Correctness (logic/edge cases), and Style (conventions).

Multi-AI Code Review

Name: Multi-AI Code Review
Author: adaptationio

byadaptationio

Seguridad y Pruebas

Performs comprehensive code quality assessments using an ensemble of specialized AI agents to ensure security, performance, and maintainability.

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Multi-AI Code Review leverages a sophisticated multi-agent architecture involving Claude, Gemini, and Codex to perform deep code analysis across five critical dimensions: Security, Performance, Maintainability, Correctness, and Style. By utilizing an 'LLM-as-judge' consensus pattern, the skill minimizes false positives by prioritizing issues where multiple models agree. This tool is ideal for teams looking to automate high-fidelity Pull Request reviews, conduct pre-release security audits, or establish consistent quality gates within their CI/CD pipelines while maintaining a human-in-the-loop workflow.

Características Principales

0 GitHub stars
Automated quality scoring and severity-based prioritization
CI/CD integration for automated PR feedback and quality gates
LLM-as-judge consensus mechanism to reduce false positive rates
5-dimensional analysis (Security, Performance, Maintainability, Correctness, Style)
Multi-agent ensemble architecture using Claude, Gemini, and Codex

Casos de Uso

Educational mentorship reviews for developer onboarding
Pre-release security and performance auditing
Automating multi-perspective Pull Request reviews

Acerca de

Características Principales

0 GitHub stars
Automated quality scoring and severity-based prioritization
CI/CD integration for automated PR feedback and quality gates
LLM-as-judge consensus mechanism to reduce false positive rates
5-dimensional analysis (Security, Performance, Maintainability, Correctness, Style)
Multi-agent ensemble architecture using Claude, Gemini, and Codex

Casos de Uso

Educational mentorship reviews for developer onboarding
Pre-release security and performance auditing
Automating multi-perspective Pull Request reviews