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Use this agent when you need a final review pass to ensure code changes are as simple and minimal as possible. This agent should be invoked after implementation is complete but before finalizing changes, to identify opportunities for simplification, remove unnecessary complexity, and ensure adherence to YAGNI principles. Examples: <example>Context: The user has just implemented a new feature and wants to ensure it's as simple as possible. user: "I've finished implementing the user authentication system" assistant: "Great! Let me review the implementation for simplicity and minimalism using the code-simplicity-reviewer agent" <commentary>Since implementation is complete, use the code-simplicity-reviewer agent to identify simplification opportunities.</commentary></example> <example>Context: The user has written complex business logic and wants to simplify it. user: "I think this order processing logic might be overly complex" assistant: "I'll use the code-simplicity-reviewer agent to analyze the complexity...
Use this agent when you need to perform security audits, vulnerability assessments, or security reviews of code. This includes checking for common security vulnerabilities, validating input handling, reviewing authentication/authorization implementations, scanning for hardcoded secrets, and ensuring OWASP compliance. <example>Context: The user wants to ensure their newly implemented API endpoints are secure before deployment.\nuser: "I've just finished implementing the user authentication endpoints. Can you check them for security issues?"\nassistant: "I'll use the security-sentinel agent to perform a comprehensive security review of your authentication endpoints."\n<commentary>Since the user is asking for a security review of authentication code, use the security-sentinel agent to scan for vulnerabilities and ensure secure implementation.</commentary></example> <example>Context: The user is concerned about potential SQL injection vulnerabilities in their database queries.\nuser: "I'm worried about SQL inj...
Use this agent when you need to understand the historical context and evolution of code changes, trace the origins of specific code patterns, identify key contributors and their expertise areas, or analyze patterns in commit history. This agent excels at archaeological analysis of git repositories to provide insights about code evolution and development patterns. <example>Context: The user wants to understand the history and evolution of recently modified files.\nuser: "I've just refactored the authentication module. Can you analyze the historical context?"\nassistant: "I'll use the git-history-analyzer agent to examine the evolution of the authentication module files."\n<commentary>Since the user wants historical context about code changes, use the git-history-analyzer agent to trace file evolution, identify contributors, and extract patterns from the git history.</commentary></example> <example>Context: The user needs to understand why certain code patterns exist.\nuser: "Why does this payment processing...
Use this agent when you need to review JavaScript or Stimulus frontend code changes with a special eye for race conditions. The agent should be invoked after implementing JavaScript features, modifying existing JavaScript code, or when creating or modifying Stimulus controllers. The agent applies Julik's eye for UI race conditions in JavaScript and Stimulus code. Examples: - <example> Context: The user has just implemented a new Stimulus controller. user: "I've created a new controller for showing and hiding toasts" assistant: "I've implemented the controller. Now let me have Julik take a look at possible race conditions and DOM irregularities." <commentary> Since new Stimulus controller code was written, use the julik-frontend-races-reviewer agent to apply Julik's uncanny knowledge of UI data races and quality checks in JavaScript and Stimulus code. </commentary> </example> - <example> Context: The user has refactored an existing Stimulus controller. user: "Please refactor the controller to slowly animate...
Query official Microsoft documentation to find concepts, tutorials, and code examples across Azure, .NET, Agent Framework, Aspire, VS Code, GitHub, and more. Uses Microsoft Learn MCP as the default, with Context7 and Aspire MCP for content that lives outside learn.microsoft.com.
Guidelines for self-explanatory code and meaningful documentation. Activate when working with comments, docstrings, documentation, code clarity, API documentation, JSDoc, or discussing code commenting strategies. Guides on why over what, anti-patterns, decision frameworks, and language-specific examples.
Guide for creating high-quality, user-friendly diagnostics in Biome. Use when implementing error messages, warnings, and code frame displays. Examples:<example>User needs to create a diagnostic for a lint rule</example><example>User wants to add helpful advice to error messages</example><example>User is improving diagnostic quality</example>
Step-by-step guide for creating and implementing lint rules in Biome's analyzer. Use when implementing rules like noVar, useConst, or any custom lint/assist rule. Examples:<example>User wants to create a rule that detects unused variables</example><example>User needs to add code actions to fix diagnostic issues</example><example>User is implementing semantic analysis for binding references</example>
Guide for working with Biome's module graph and type inference system. Use when implementing type-aware lint rules or working on TypeScript support. Examples:<example>User needs to understand type resolution for a lint rule</example><example>User is working on the module graph infrastructure</example><example>User wants to implement type inference for a new feature</example>
Generates comprehensive API documentation in Markdown, HTML, or Docusaurus format from Express, Next.js, Fastify, or other API routes. Creates endpoint references, request/response examples, authentication guides, and error documentation. Use when users request "generate api docs", "api documentation", "endpoint documentation", or "api reference".
Query the bundled research knowledge graph for methodology guidance. Routes questions through a 3-tier knowledge base — WHY (research claims), HOW (guidance docs), WHAT IT LOOKS LIKE (domain examples) — plus structured reference documents. Returns research-backed answers grounded in specific claims with practical application to the user's system. Triggers on "/ask", "/ask [question]", "why does my system...", "how should I...".
Run holistic pedagogical review on lecture slides. Checks narrative arc, student prerequisites, worked examples, notation clarity, and deck pacing.