review
Comprehensive code review with failure scenario analysis, security checks, and prioritized fix suggestions. Use after /execute to validate implementation quality.
Skills matching the name, description or GitHub author.
Comprehensive code review with failure scenario analysis, security checks, and prioritized fix suggestions. Use after /execute to validate implementation quality.
Orchestrate multi-agent development workflows. Use when starting a new feature to coordinate architecture design, implementation, code review, and release through specialized sub-agents.
Apply a production-grade code review checklist for Java backend changes: layering, error handling, security, tests, observability, performance, and maintainability. Use when preparing a PR or reviewing one.
Prepare structured PR reviews using a three-phase framework (Context, Analysis, Validation). Use when reviewing pull requests, when user shares a PR URL, or mentions "review PR", "code review", or "check this P...
Infrastructure as Code review methodology. Use when auditing Terraform/OpenTofu configurations for security, reliability, and best practices. Focuses on HOW TO REVIEW IaC, not how to write it.
Orchestrates pitfall prevention skills for common development issues. Auto-triggered during code review to check for TanStack Query, Drizzle ORM, Express API, React, WebSocket, blockchain RPC, and security pitf...
Inspire team coding standards, best practices, and code review guidelines. Use when writing code, reviewing PRs, or ensuring code quality.
Automated code review analyzing security, performance, maintainability, and test coverage. Activated during code reviews or when conducting analysis.
Review mode for code review, security audit, and best practices check. Use when user asks for code review or quality assessment. Evaluates code quality, identifies issues by severity, and suggests improvements...
Automated code review for pull requests using specialized review patterns. Analyzes code for quality, security, performance, and best practices. Use when reviewing code changes, PRs, or doing code audits.
Comprehensive code review expertise. Use when reviewing code, evaluating architecture, or assessing quality. Triggers on review, evaluate, assess, audit, code quality, best practices.
Automated code review for pull requests using specialized review patterns. Analyzes code for quality, security, performance, and best practices. Use when reviewing code changes, PRs, or doing code audits.
Use the lumen CLI for git diffs, AI-generated commit messages, change explanations, and git command generation. Triggers on diff viewing, commit drafting, code review, explaining git changes, or generating git...
Systematic multi-perspective code review with consistent quality gates.
Orchestrates the Code Review process (Analysis -> Fix -> Reply/Close). Manages the workflow by invoking specialized sub-skills like code-review-analysis, tdd-refactoring, and tdd-audit. Used for (1) addressing...
Perform code reviews using GitHub Copilot CLI to identify bugs, security vulnerabilities, performance issues, and code quality problems. Use when the user asks to review code, check for issues, security audit,...
Run Phase 4 verification with Cursor and Gemini code review.
Automated code review for pull requests using multiple specialized agents with confidence-based scoring to filter false positives.
Use when executing implementation plans with independent tasks in the current session - dispatches fresh subagent for each task with code review between tasks, enabling fast iteration with quality gates
Perform language-agnostic code reviews focusing on correctness, regressions, security, and tests. Use when the user asks for a review.
Setup and troubleshoot GitHub Actions workflows using anthropics/claude-code-action for automated PR reviews. Use when creating CI/CD workflows that leverage Claude for code review, research validation, or auto...
The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.