pr-review-toolkit

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 CLAUDE.md. Also the agent needs to know which files to focus on for the review. In most cases this will be recently completed work which is unstaged in git (can be retrieved by running git diff). However there can be cases where this is different, make sure to specify this as the agent input when calling the agent. Typical triggers include the user asking for a review of a feature they just implemented, the assistant proactively reviewing its own newly-written code before declaring a task done, and a final pre-PR check before opening a pull request. See "When to invoke" in the agent body for worked scenarios.

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, and (4) when you need to verify that comments accurately reflect the code they describe. See "When to invoke" in the agent body for worked scenarios.

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. Typical triggers include the user asking whether tests on a freshly-created PR are thorough, an updated PR adding new logic that needs coverage analysis, and a final pre-merge double-check before marking a PR ready. See "When to invoke" in the agent body for worked scenarios.

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>

Reviews code changes in the working tree, current branch, or PR. Finds bugs, security issues, performance problems, style violations, and test coverage gaps.

Multi-agent review — adaptively dispatches 1-3 agents (code-review + security-audit + test-analyzer) based on complexity, validates findings, consolidates report. Use for comprehensive review before deploy or merging large PRs.

This skill should be used when the user asks to review a GitHub pull request AND post the findings as a comment on the PR ('review and comment on PR', 'review PR roi comment len do'). Multi-agent confidence scoring, threshold 80/100, posts via gh. For local working-tree review without posting, use the code-review skill.

Comprehensive PR review using specialized agents, each focusing on a different aspect of code quality.