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Create or edit agent skills with expert guidance on structure and best practices

Create a new prompt that another Claude can execute

Research topics with Readwise CLI and produce Obsidian-ready notes

Conduct in-depth code research using tiered tool strategy

Delegate one or more prompts to fresh sub-task contexts with parallel or sequential execution

Use this agent when you need to review code for potential improvements in readability, performance, or adherence to best practices. This includes after completing a feature implementation, during code review preparation, when refactoring legacy code, or when you want a second opinion on code quality. Examples:\n\n<example>\nContext: User just finished implementing a new feature and wants to ensure code quality.\nuser: "I just finished the user authentication module. Can you review it?"\nassistant: "I'll use the code-improvement-scanner agent to analyze your authentication module for potential improvements."\n<commentary>\nSince the user completed a significant piece of code and is asking for review, use the code-improvement-scanner agent to provide detailed improvement suggestions.\n</commentary>\n</example>\n\n<example>\nContext: User is working on optimizing application performance.\nuser: "The API response times are slow. Can you look at the handlers?"\nassistant: "Let me use the code-improvement-scanner agent to analyze your API handlers for performance improvements and optimization opportunities."\n<commentary>\nThe user is concerned about performance, so the code-improvement-scanner agent should be used to identify performance bottlenecks and suggest optimizations.\n</commentary>\n</example>\n\n<example>\nContext: User wants to improve code before a pull request.\nuser: "I'm about to submit a PR for the payment processing changes. Any suggestions?"\nassistant: "I'll run the code-improvement-scanner agent on your payment processing changes to identify any improvements before your PR submission."\n<commentary>\nBefore a PR submission is an ideal time to use the code-improvement-scanner agent to catch issues and improve code quality.\n</commentary>\n</example>

Use this agent when you need to review code for adherence to project guidelines, style guides, and best practices. This agent should be used proactively after writing or modifying code, especially before committing changes or creating pull requests. It will check for style violations, potential issues, and ensure code follows the established patterns in AGENTS.md. Also the agent needs to know which files to focus on for the review. In most cases this will recently completed work which is unstaged in git (can be retrieved by doing a git diff). However there can be cases where this is different, make sure to specify this as the agent input when calling the agent. Examples: <example> Context: The user has just implemented a new feature with several TypeScript files. user: "I've added the new authentication feature. Can you check if everything looks good?" assistant: "I'll use the Task tool to launch the code-reviewer agent to review your recent changes." <commentary> Since the user has completed a feature and wants validation, use the code-reviewer agent to ensure the code meets project standards. </commentary> </example> <example> Context: The assistant has just written a new utility function. user: "Please create a function to validate email addresses" assistant: "Here's the email validation function:" <function call omitted for brevity> assistant: "Now I'll use the Task tool to launch the code-reviewer agent to review this implementation." <commentary> Proactively use the code-reviewer agent after writing new code to catch issues early. </commentary> </example> <example> Context: The user is about to create a PR. user: "I think I'm ready to create a PR for this feature" assistant: "Before creating the PR, I'll use the Task tool to launch the code-reviewer agent to ensure all code meets our standards." <commentary> Proactively review code before PR creation to avoid review comments and iterations. </commentary> </example>

Simplifies and refines code for clarity, consistency, and maintainability while preserving all functionality. Focuses on recently modified code unless instructed otherwise.

Use this agent when code has been written or modified and needs to be simplified for clarity, consistency, and maintainability while preserving all functionality. This agent should be triggered automatically after completing a coding task or writing a logical chunk of code. It simplifies code by following project best practices while retaining all functionality. The agent focuses only on recently modified code unless instructed otherwise. Examples: <example> Context: The assistant has just implemented a new feature that adds user authentication to an API endpoint. user: "Please add authentication to the /api/users endpoint" assistant: "I've implemented the authentication for the /api/users endpoint. Here's the code:" <function call omitted for brevity> <commentary> Since a logical chunk of code has been written (authentication feature), use the Task tool to launch the code-simplifier agent to improve the code's clarity and maintainability while preserving functionality. </commentary> assistant: "Now let me use the code-simplifier agent to refine this implementation for better clarity and maintainability" </example> <example> Context: The assistant has just fixed a bug by adding several conditional checks. user: "Fix the null pointer exception in the data processor" assistant: "I've added the necessary null checks to prevent the exception:" <function call omitted for brevity> <commentary> After modifying code to fix a bug, use the code-simplifier agent to ensure the fix follows best practices and maintains code quality. </commentary> assistant: "Let me refine this bug fix using the code-simplifier agent to ensure it follows our best practices" </example> <example> Context: The assistant has just refactored a function to improve performance. user: "Optimize the data sorting algorithm for better performance" assistant: "I've optimized the sorting algorithm. Here's the updated implementation:" <function call omitted for brevity> <commentary> After completing a performance optimization task, use the code-simplifier agent to ensure the optimized code is also clear and maintainable. </commentary> assistant: "Now I'll use the code-simplifier agent to ensure the optimized code is also clear and follows our coding standards" </example>

Use this agent when you need to analyze code comments for accuracy, completeness, and long-term maintainability. This includes: (1) After generating large documentation comments or docstrings, (2) Before finalizing a pull request that adds or modifies comments, (3) When reviewing existing comments for potential technical debt or comment rot, (4) When you need to verify that comments accurately reflect the code they describe. <example> Context: The user is working on a pull request that adds several documentation comments to functions. user: "I've added documentation to these functions. Can you check if the comments are accurate?" assistant: "I'll use the comment-analyzer agent to thoroughly review all the comments in this pull request for accuracy and completeness." <commentary> Since the user has added documentation comments and wants them checked, use the comment-analyzer agent to verify their accuracy against the actual code. </commentary> </example> <example> Context: The user just asked to generate comprehensive documentation for a complex function. user: "Add detailed documentation for this authentication handler function" assistant: "I've added the documentation. Now let me use the comment-analyzer agent to verify that the comments are accurate and helpful for long-term maintenance." <commentary> After generating large documentation comments, proactively use the comment-analyzer to ensure quality. </commentary> </example> <example> Context: The user is preparing to create a pull request with multiple code changes and comments. user: "I think we're ready to create the PR now" assistant: "Before creating the pull request, let me use the comment-analyzer agent to review all the comments we've added or modified to ensure they're accurate and won't create technical debt." <commentary> Before finalizing a PR, use the comment-analyzer to review all comment changes. </commentary> </example>

Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction.

Write and maintain Architecture Decision Records (ADRs) following best practices for technical decision documentation. Use when documenting significant technical decisions, reviewing past architectural choices, or establishing decision processes.

Guide for writing ast-grep rules to perform structural code search and analysis. Use when users need to search codebases using Abstract Syntax Tree (AST) patterns, find specific code structures, or perform complex code queries that go beyond simple text search. This skill should be used when users ask to search for code patterns, find specific language constructs, or locate code with particular structural characteristics.

Master Python asyncio, concurrent programming, and async/await patterns for high-performance applications. Use when building async APIs, concurrent systems, or I/O-bound applications requiring non-blocking operations.

Master defensive Bash programming techniques for production-grade scripts. Use when writing robust shell scripts, CI/CD pipelines, or system utilities requiring fault tolerance and safety.