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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>

Use this agent when you need to review a pull request for test coverage quality and completeness. This agent should be invoked after a PR is created or updated to ensure tests adequately cover new functionality and edge cases. Examples: <example> Context: Daisy has just created a pull request with new functionality. user: "I've created the PR. Can you check if the tests are thorough?" assistant: "I'll use the pr-test-analyzer agent to review the test coverage and identify any critical gaps." <commentary> Since Daisy is asking about test thoroughness in a PR, use the Task tool to launch the pr-test-analyzer agent. </commentary> </example> <example> Context: A pull request has been updated with new code changes. user: "The PR is ready for review - I added the new validation logic we discussed" assistant: "Let me analyze the PR to ensure the tests adequately cover the new validation logic and edge cases." <commentary> The PR has new functionality that needs test coverage analysis, so use the pr-test-analyzer agent. </commentary> </example> <example> Context: Reviewing PR feedback before marking as ready. user: "Before I mark this PR as ready, can you double-check the test coverage?" assistant: "I'll use the pr-test-analyzer agent to thoroughly review the test coverage and identify any critical gaps before you mark it ready." <commentary> Daisy wants a final test coverage check before marking PR ready, use the pr-test-analyzer agent. </commentary> </example>

Use this agent when you want a brutally honest, no-holds-barred code review that will expose every flaw, questionable decision, and architectural weakness in recently written code. This agent should be invoked after completing a feature, function, or logical chunk of code that needs rigorous scrutiny. Examples:\n\n<example>\nContext: The user just finished implementing a new API endpoint.\nuser: "I just wrote this REST endpoint for user authentication"\nassistant: "Let me review this code with the ruthless-code-reviewer agent to ensure it meets the highest standards."\n<Task tool invocation to launch ruthless-code-reviewer>\n</example>\n\n<example>\nContext: A developer wants feedback on their data structure choices.\nuser: "Can you review the data model I created for the shopping cart?"\nassistant: "I'll invoke the ruthless-code-reviewer agent to tear apart your design decisions and ensure you haven't made any amateur mistakes."\n<Task tool invocation to launch ruthless-code-reviewer>\n</example>\n\n<example>\nContext: Code was just written and user wants quality assurance.\nuser: "I think I'm done with this function, can you check it?"\nassistant: "Time to face the music. Let me bring in the ruthless-code-reviewer to show you what 'done' actually means."\n<Task tool invocation to launch ruthless-code-reviewer>\n</example>

Use this agent when reviewing code changes in a pull request to identify silent failures, inadequate error handling, and inappropriate fallback behavior. This agent should be invoked proactively after completing a logical chunk of work that involves error handling, catch blocks, fallback logic, or any code that could potentially suppress errors. Examples: <example> Context: Daisy has just finished implementing a new feature that fetches data from an API with fallback behavior. Daisy: "I've added error handling to the API client. Can you review it?" Assistant: "Let me use the silent-failure-hunter agent to thoroughly examine the error handling in your changes." <Task tool invocation to launch silent-failure-hunter agent> </example> <example> Context: Daisy has created a PR with changes that include try-catch blocks. Daisy: "Please review PR #1234" Assistant: "I'll use the silent-failure-hunter agent to check for any silent failures or inadequate error handling in this PR." <Task tool invocation to launch silent-failure-hunter agent> </example> <example> Context: Daisy has just refactored error handling code. Daisy: "I've updated the error handling in the authentication module" Assistant: "Let me proactively use the silent-failure-hunter agent to ensure the error handling changes don't introduce silent failures." <Task tool invocation to launch silent-failure-hunter agent> </example>

Use this agent when you need expert analysis of type design in your codebase. Specifically use it: (1) when introducing a new type to ensure it follows best practices for encapsulation and invariant expression, (2) during pull request creation to review all types being added, (3) when refactoring existing types to improve their design quality. The agent will provide both qualitative feedback and quantitative ratings on encapsulation, invariant expression, usefulness, and enforcement. <example> Context: Daisy is writing code that introduces a new UserAccount type and wants to ensure it has well-designed invariants. user: "I've just created a new UserAccount type that handles user authentication and permissions" assistant: "I'll use the type-design-analyzer agent to review the UserAccount type design" <commentary> Since a new type is being introduced, use the type-design-analyzer to ensure it has strong invariants and proper encapsulation. </commentary> </example> <example> Context: Daisy is creating a pull request and wants to review all newly added types. user: "I'm about to create a PR with several new data model types" assistant: "Let me use the type-design-analyzer agent to review all the types being added in this PR" <commentary> During PR creation with new types, use the type-design-analyzer to review their design quality. </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.

Master binary analysis patterns including disassembly, decompilation, control flow analysis, and code pattern recognition. Use when analyzing executables, understanding compiled code, or performing static analysis on binaries.

You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.

Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, or extract information from web pages.

Browser automation, debugging, and performance analysis using Puppeteer CLI scripts. Use for automating browsers, taking screenshots, analyzing performance, monitoring network traffic, web scraping, form automation, and JavaScript debugging.

Use when building CLI tools, implementing argument parsing, or adding interactive prompts. Invoke for CLI design, argument parsing, interactive prompts, progress indicators, shell completions.

Conducts in-depth code research using a tiered tool strategy. Use when investigating codebases, researching libraries/APIs, debugging errors, or understanding unfamiliar code patterns.

Implement Command Query Responsibility Segregation for scalable architectures. Use when separating read and write models, optimizing query performance, or building event-sourced systems.

Expert guidance for creating, writing, building, and refining agent skills. Use when working with SKILL.md files, authoring new skills, improving existing skills, or understanding skill structure and best practices.

Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies

Master end-to-end testing with Playwright and Cypress to build reliable test suites that catch bugs, improve confidence, and enable fast deployment. Use when implementing E2E tests, debugging flaky tests, or establishing testing standards.

Master error handling patterns across languages including exceptions, Result types, error propagation, and graceful degradation to build resilient applications. Use when implementing error handling, designing APIs, or improving application reliability.

Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.

Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup

Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, or applications. Generates creative, polished code that avoids generic AI aesthetics.

Gemini CLI for one-shot Q&A, summaries, and generation.

GitHub CLI (gh) comprehensive reference for repositories, issues, pull requests, Actions, projects, releases, gists, codespaces, organizations, extensions, and all GitHub operations from the command line.

Reverse engineer binaries using Ghidra's headless analyzer. Decompile executables, extract functions, strings, symbols, and analyze call graphs without GUI.

Master Go concurrency with goroutines, channels, sync primitives, and context. Use when building concurrent Go applications, implementing worker pools, or debugging race conditions.

Complete guide for Go backend development including concurrency patterns, web servers, database integration, microservices, and production deployment

Design LLM applications using LangChain 1.x and LangGraph for agents, memory, and tool integration. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.

Use this skill for requests related to LangGraph in order to fetch relevant documentation to provide accurate, up-to-date guidance.

Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.

Implement memory-safe programming with RAII, ownership, smart pointers, and resource management across Rust, C++, and C. Use when writing safe systems code, managing resources, or preventing memory bugs.

Design microservices architectures with service boundaries, event-driven communication, and resilience patterns. Use when building distributed systems, decomposing monoliths, or implementing microservices.

Interact with Obsidian vaults using the Obsidian CLI to read, create, search, and manage notes, tasks, properties, and more. Also supports plugin and theme development with commands to reload plugins, run JavaScript, capture errors, take screenshots, and inspect the DOM. Use when the user asks to interact with their Obsidian vault, manage notes, search vault content, perform vault operations from the command line, or develop and debug Obsidian plugins and themes.

Create and edit Obsidian Flavored Markdown with wikilinks, embeds, callouts, properties, and other Obsidian-specific syntax. Use when working with .md files in Obsidian, or when the user mentions wikilinks, callouts, frontmatter, tags, embeds, or Obsidian notes.

Use when the user asks how to build with OpenAI products or APIs and needs up-to-date official documentation with citations (for example: Codex, Responses API, Chat Completions, Apps SDK, Agents SDK, Realtime, model capabilities or limits); prioritize OpenAI docs MCP tools and restrict any fallback browsing to official OpenAI domains.

Debug complex issues using competing hypotheses with parallel investigation, evidence collection, and root cause arbitration. Use this skill when debugging bugs with multiple potential causes, performing root cause analysis, or organizing parallel investigation workflows.

Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information from web pages.

Design a PostgreSQL-specific schema. Covers best-practices, data types, indexing, constraints, performance patterns, and advanced features

Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates.

Common Python anti-patterns to avoid. Use as a checklist when reviewing code, before finalizing implementations, or when debugging issues that might stem from known bad practices.

Python background job patterns including task queues, workers, and event-driven architecture. Use when implementing async task processing, job queues, long-running operations, or decoupling work from request/response cycles.

Python code style, linting, formatting, naming conventions, and documentation standards. Use when writing new code, reviewing style, configuring linters, writing docstrings, or establishing project standards.

Python configuration management via environment variables and typed settings. Use when externalizing config, setting up pydantic-settings, managing secrets, or implementing environment-specific behavior.

Python design patterns including KISS, Separation of Concerns, Single Responsibility, and composition over inheritance. Use when making architecture decisions, refactoring code structure, or evaluating when abstractions are appropriate.

Python error handling patterns including input validation, exception hierarchies, and partial failure handling. Use when implementing validation logic, designing exception strategies, handling batch processing failures, or building robust APIs.

Python observability patterns including structured logging, metrics, and distributed tracing. Use when adding logging, implementing metrics collection, setting up tracing, or debugging production systems.

Profile and optimize Python code using cProfile, memory profilers, and performance best practices. Use when debugging slow Python code, optimizing bottlenecks, or improving application performance.

Python resilience patterns including automatic retries, exponential backoff, timeouts, and fault-tolerant decorators. Use when adding retry logic, implementing timeouts, building fault-tolerant services, or handling transient failures.

Python resource management with context managers, cleanup patterns, and streaming. Use when managing connections, file handles, implementing cleanup logic, or building streaming responses with accumulated state.

Implement comprehensive testing strategies with pytest, fixtures, mocking, and test-driven development. Use when writing Python tests, setting up test suites, or implementing testing best practices.

Python type safety with type hints, generics, protocols, and strict type checking. Use when adding type annotations, implementing generic classes, defining structural interfaces, or configuring mypy/pyright.

Surgical code refactoring to improve maintainability without changing behavior. Covers extracting functions, renaming variables, breaking down god functions, improving type safety, eliminating code smells, and applying design patterns. Less drastic than repo-rebuilder; use for gradual improvements.

Use the Readwise CLI to run relevance-first research across Reader documents and highlights, then synthesize an Obsidian-ready note with source-linked evidence and explicit confidence gaps. Use when researching topics from Readwise content for PKM workflows in Obsidian.

Autonomous skill generator that learns new technologies from the web. Use when, users want to learn about a new library/framework/tool, need to create a skill for an unfamiliar technology, want to research and document a technology's usage patterns, or invoke with `/learn <topic>`. This skill uses web search and browser tools to discover, extract, and synthesize documentation into a reusable skill.

Use when building Spring Boot 3.x applications, microservices, or reactive Java applications. Invoke for Spring Data JPA, Spring Security 6, WebFlux, Spring Cloud integration.

Master SQL query optimization, indexing strategies, and EXPLAIN analysis to dramatically improve database performance and eliminate slow queries. Use when debugging slow queries, designing database schemas, or optimizing application performance.

Use when executing implementation plans with independent tasks in the current session

Decompose complex tasks, design dependency graphs, and coordinate multi-agent work with proper task descriptions and workload balancing. Use this skill when breaking down work for agent teams, managing task dependencies, or monitoring team progress.

Use when implementing any feature or bugfix, before writing implementation code

Remote control tmux sessions for interactive CLIs (python, gdb, etc.) by sending keystrokes and scraping pane output.

Guide AI agents to use Jujutsu (jj) for version control. Covers core concepts, daily workflows, Git compatibility, conflict resolution, and collaboration with GitHub. Use this skill when working with jj repositories or when the user wants to use Jujutsu instead of Git.

Use when starting feature work that needs isolation from current workspace or before executing implementation plans - creates isolated git worktrees with smart directory selection and safety verification

Master the uv package manager for fast Python dependency management, virtual environments, and modern Python project workflows. Use when setting up Python projects, managing dependencies, or optimizing Python development workflows with uv.