Code Review & Quality for Claude Code

Configure comprehensive code formatting tools with consistent style enforcement

Configure comprehensive code linting and quality analysis tools with automated enforcement

Evaluate Agent Skill design quality against official specifications and best practices. Use when reviewing, auditing, or improving SKILL.md files and skill packages. Provides multi-dimensional scoring and actionable improvement suggestions.

Use this agent when conducting security audits of smart contracts. Specializes in vulnerability detection, attack vector analysis, and comprehensive security assessments. Examples: <example>Context: User needs to audit a DeFi protocol user: 'Can you audit my yield farming contract for security issues?' assistant: 'I'll use the smart-contract-auditor agent to perform a comprehensive security audit, checking for reentrancy, overflow issues, and economic attacks' <commentary>Security audits require

Guide for quality focused software architecture. This skill should be used when users want to write code, design architecture, analyze code, in any case that relates to software development.

Expert-level software engineering agent. Deliver production-ready, maintainable code. Execute systematically and specification-driven. Document comprehensively. Operate autonomously and adaptively.

Audit a project for software supply chain risk, including dependency vulnerabilities, lockfile issues, malicious package indicators, SBOM coverage, and license concerns.

Audit and improve accessibility in Svelte/SvelteKit applications, ensuring WCAG compliance and inclusive user experiences.

Migrate Svelte/SvelteKit projects between versions, adopt new features like runes, and handle breaking changes.

Review and fix Swift concurrency issues such as actor isolation and Sendable violations.

Implement minimal code to satisfy GitHub issue requirements and make failing tests pass without over-engineering.

Guide test-first development by writing failing tests that describe desired behaviour from GitHub issue context before implementation exists.

Improve code quality, apply security best practices, and enhance design whilst maintaining green tests and GitHub issue compliance.

Test-Driven Development workflow principles. RED-GREEN-REFACTOR cycle.

Generate technical debt remediation plans for code, tests, and documentation.

Expert technical debt analyst for code health, maintainability, and strategic refactoring planning. Use PROACTIVELY when codebase shows complexity growth, when planning sprints, or when prioritizing engineering work.

Use this agent when you need to analyze code repositories, technical documentation, implementation details, or evaluate technical solutions. This includes researching GitHub projects, reviewing API documentation, finding code examples, assessing code quality, tracking version histories, or comparing technical implementations. <example>Context: The user wants to understand different implementations of a rate limiting algorithm. user: "I need to implement rate limiting in my API. What are the best

Act as an Azure Terraform Infrastructure as Code coding specialist that creates and reviews Terraform for Azure resources.

Terraform-focused agent that reviews and creates safer IaC changes with emphasis on state safety, least privilege, module patterns, drift detection, and plan/apply discipline

Analyze and improve test coverage with comprehensive reporting and gap identification

Test automation and quality assurance specialist. Use PROACTIVELY for test strategy, test automation, coverage analysis, CI/CD testing, and quality engineering practices.

Run tests and systematically fix all failing tests using smart error grouping. Use when user asks to fix failing tests, mentions test failures, runs test suite and failures occur, or requests to make tests pass.

Analyzes code changes and generates comprehensive test cases by understanding existing test patterns, edge cases, and testing conventions in the codebase

Analyze test suite quality with comprehensive metrics and improvement recommendations

Executes tests, analyzes results, identifies failures, diagnoses root causes, and provides actionable fixes for failing tests

Use when implementing TypeScript code requiring advanced type system patterns, complex generics, type-level programming, or end-to-end type safety across full-stack applications. Specifically:\\n\\n<example>\\nContext: Building an API client library that needs maximum type safety with generic request/response handling and discriminated unions for different API outcomes\\nuser: \"Create a type-safe API client library using TypeScript where callers get full type inference for requests and response

Detects and removes unused code (imports, functions, classes) across multiple languages. Use PROACTIVELY after refactoring, when removing features, or before production deployment.

Systematically update project documentation with implementation status, API changes, and synchronized content

This skill enables visual inspection of websites running locally or remotely to identify and fix design issues. Triggers on requests like "review website design", "check the UI", "fix the layout", "find design problems". Detects issues with responsive design, accessibility, visual consistency, and layout breakage, then performs fixes at the source code level.

Comprehensive web quality audit covering performance, accessibility, SEO, and best practices. Use when asked to "audit my site", "review web quality", "run lighthouse audit", "check page quality", or "optimize my website".

Ask WG Code Alchemist to transform your code with Clean Code principles and SOLID design

Ask WG Code Sentinel to review your code for security issues.

Write comprehensive unit and integration tests with proper mocking and coverage

Reverse-engineer an AI-built codebase into the system documents reviewers and auditors need — a core set (architecture, flows, permissions, variables) plus conditional docs (emails, cron, SEO, automation) when they apply

The method for finding the gap between what a system is supposed to do and what the code actually does — the class of bug generic scanners miss because they have no model of intent. Defines what counts as documented intent, what counts as implementation evidence, which mismatches matter, and how to avoid hand-wavy findings. Use when auditing AI-built code, reviewing access control against documented permissions, or checking whether a codebase matches its own documentation.

Static performance audit of AI-built code — find N+1 queries and request waterfalls, over-fetching, missing indexes, and caching opportunities, ranked by effort and impact

Run a pre-mortem risk analysis on a PRD, launch plan, or feature — identify what could go wrong before it does

Static security audit of AI-built code — map trust boundaries, cross-reference documented intent, self-refute every finding, and report only evidence-backed risks