code review Agent Skills

code-review is a mandatory review workflow for code changes before commits and deploys. It helps teams run a structured /code-review step, choose an engine, and get actionable feedback instead of a generic pass. Ideal for pre-merge checks, release candidates, and high-stakes refactors.

code-review-excellence helps agents produce clearer, more constructive code reviews with better prioritization, reviewer tone, and actionable feedback for pull requests, mentoring, and team review standards.

code-review is an AI-powered CodeRabbit skill for reviewing changed code, PRs, staged commits, and diff ranges. It helps catch bugs, security issues, and quality risks, then groups findings by severity so agents can act on them quickly. Use it for structured code review instead of generic critique.

code-review-and-quality is a structured pre-merge review skill that checks correctness, readability, architecture, security, and performance. Install it from the parent repo, read skills/code-review-and-quality/SKILL.md, and use it with diffs, task context, and test results for stronger review decisions.

flutter-dart-code-review is a library-agnostic Flutter and Dart code review checklist for architecture, widget quality, state management, performance, accessibility, security, and clean code. Use it as a structured flutter-dart-code-review guide for Code Review across BLoC, Riverpod, Provider, GetX, MobX, Signals, or custom patterns.

requesting-code-review is a lightweight workflow for dispatching the superpowers:code-reviewer subagent with a clean git diff, requirements, and change summary so reviews happen at the right time and produce actionable, severity-ranked feedback before merge.

receiving-code-review helps you verify PR feedback before editing code. Use it to restate review comments, check them against the codebase, ask for clarification on unclear items, and push back when suggestions do not fit.

Review skill for pre-landing PR review. Use it to check diffs against the base branch for SQL safety, trust-boundary issues, shell injection, enum completeness, and other structural risks. Best for a review guide that helps agents flag real defects fast with less guesswork than a generic prompt.

gemini-review is a Gemini-powered code review skill for large repositories and PRs. It uses Gemini 2.5 Pro and 1M-token context to review code with less chunking, better cross-file reasoning, and CI/CD-friendly feedback.

plan-eng-review is an interactive engineering plan review skill for locking down architecture, data flow, edge cases, test coverage, performance, and rollout risk before coding begins. Use the plan-eng-review skill when you have a design doc or implementation plan and want sharper feedback than a generic review prompt.

codex-review is a GitHub skill for OpenAI Codex CLI code review with GPT-5.2-Codex. It helps teams catch bugs, security issues, and code quality problems with structured findings, GitHub-native review flows, and CI/CD-friendly usage.

code-reviewer is an AI code review skill that follows a strict review order: security, performance, correctness, and maintainability. It uses rule files for SQL injection, XSS, N+1 queries, error handling, naming, and type hints, making PR reviews more consistent than a generic review prompt.

github-pr-review is a GitHub PR review skill for collecting inline comments, PR-level review bodies, and replies, then organizing feedback by severity so you can fix blockers first. Use it to resolve PR comments, respond to reviewers, and update the branch with targeted commits and thread replies. It fits the github-pr-review guide for authenticated GitHub CLI workflows.

code-reviewer is a lightweight skill for Code Review that turns code or diffs into a structured report covering security, performance, best practices, severity, affected lines or sections, recommended fixes, and an overall quality score.

autofix helps safely turn CodeRabbit PR review-thread feedback into validated code changes on the current GitHub branch. Use this autofix skill when you need a branch-aware CodeRabbit for Code Review workflow with explicit approval, not a generic prompt-following fixer. It checks repo state, reads trusted instructions, and applies only verified fixes.

differential-review is a security-focused code review skill for PRs, commits, and diffs. It uses baseline history, blast radius, test coverage, and structured reporting to help catch regressions in auth, crypto, external calls, and other high-risk paths. Use it for differential-review for Code Review when you need evidence-backed findings.

The code-reviewer skill guides structured PR and diff reviews for correctness, security, performance, testing, and maintainability, using repository references and a checklist script to make Code Review more consistent and actionable.

spec-to-code-compliance verifies that code matches written specifications exactly for blockchain audits and Compliance Review. Use the spec-to-code-compliance skill to compare whitepapers, design docs, and implementations, identify missing behavior, and flag undocumented or divergent logic.

naming-analyzer reviews variables, functions, classes, files, database fields, and API names, flagging vague or misleading identifiers and suggesting clearer, convention-aware alternatives for code review and refactoring.

critique is a report-only review skill that uses multiple specialized judges, debate, and consensus to assess completed work. It helps with critique for Code Review, correctness, quality, and missed issues before merging. Install critique in the NeoLabHQ context-engineering-kit and use it with file paths, commits, or context.

Use the security-review skill to review auth, user input, secrets, APIs, payments, uploads, and other sensitive flows. It provides a practical security-review guide with clear pass/fail checks, risky-pattern examples, and a focused process for catching common issues before release.

codex is an OpenAI Codex CLI wrapper skill for code review, adversarial challenge, and consult workflows. Use codex review for pass/fail review gates, codex challenge to stress-test ideas, and ask codex for follow-up reasoning with session continuity. It is best when you have a diff or concrete question.

The coding-standards skill gives a baseline for naming, readability, immutability, consistency, and code review across projects, before applying framework-specific rules.

ai-first-engineering is a concise operating model for teams where AI agents generate much of the implementation work. It helps set Agent Standards for planning, architecture, review, and testing, with guidance on install, usage, and when to apply the skill.

repo-scan is a cross-stack source audit skill that classifies files, detects embedded third-party libraries, and helps you judge what is core, duplicated, or dead weight. It is useful for repo-scan for Code Review, legacy migrations, and refactor planning. See repo-scan install and repo-scan usage guidance in the skill.

python-expert is a GitHub skill for Python code generation, review, debugging, and refactoring. It guides agents with a clear priority order—correctness, type safety, performance, then style—and points users to SKILL.md, AGENTS.md, and rule files for practical adoption.

icpg adds a WHY layer to code understanding with ReasonNodes, formal contracts, and drift detection. Use it for code review, refactoring, and pre-task analysis when you need intent, ownership, and risk context before changing code.

python-code-style helps with Python formatting, linting, naming, type hints, and docstrings. Use it to review pull requests, standardize team conventions, and set up ruff, mypy, or pyright guidance in pyproject.toml.

The gemini skill helps agents use Gemini CLI for code review, plan review, and large-context analysis. Learn when to install the skill, choose a model, avoid non-interactive approval hangs, and run safer Gemini workflows for multi-file reviews.

The python-patterns skill helps you write, review, and refactor Python code with idiomatic patterns, readable structure, type hints, and practical exception handling. Use it for new code, package/module design, or cleaner refactors that preserve behavior and follow Python conventions.

python-anti-patterns is a Python code review checklist for spotting fragile patterns like scattered retries, timeout duplication, and hidden complexity before merge, during refactoring, or while debugging.

The investigate skill guides systematic debugging and root-cause analysis for broken, flaky, or unexpected behavior. Use it for code review, incident triage, bug fixes, and "it worked yesterday" cases when you need evidence before changing code. It follows a four-phase workflow: investigate, analyze, hypothesize, implement.

golang-patterns is a practical guide for idiomatic Go patterns, code review, and refactoring. It helps Backend Development teams choose clear APIs, safe error handling, useful zero values, and maintainable package boundaries. Install golang-patterns when you need less guesswork and more consistent Go design decisions.

frontend-design-review is a GitHub skill for reviewing frontend UI work and creating distinctive, production-grade interfaces from scratch. It helps assess design system compliance, accessibility, visual quality, and whether a UI feels generic or intentionally designed. Use it for PR reviews, component reviews, and frontend-design-review for UI Design.

multi-reviewer-patterns helps agents run parallel code reviews across security, performance, architecture, testing, and accessibility, then deduplicate findings, calibrate severity, and deliver one consolidated report. Includes install context, key files, and practical usage guidance.

The java-coding-standards skill provides practical guidance for readable, maintainable Java 17+ in Spring Boot services, covering naming, immutability, Optional, streams, exceptions, generics, and package layout. Use it for coding, refactoring, and java-coding-standards for Code Review.