Essential command-line tools and system utilities.
Provides code formatting best practices and granular edit operation guidelines. Use when formatting code, making edits to files, or when determining the proper approach for code modifications. Emphasizes using external formatting tools and breaking down large modifications into atomic, sequential edits for reliability.
Reviews Python code for PEP8 compliance, type hints, modern best practices (Python 3.9+), and code quality. Detects anti-patterns, validates documentation, and provides severity-based recommendations. Integrates with modern tooling (ruff, mypy, uv).
Automatic code review and fixes before showing to user. Use when: code generation is complete, before preview. Triggers: internal use only.
Comprehensive code quality review focusing on DRY (Don't Repeat Yourself), KISS (Keep It Simple, Stupid), and Clean Code (Easy to Read) principles. Use when reviewing code changes, analyzing code quality, or assessing code maintainability for any feature, bug fix, or refactoring task.
Enforces project React coding conventions automatically when creating or modifying React components. This skill should be used proactively whenever working with .tsx/.jsx files to ensure consistent code style, naming conventions, component structure, and TypeScript patterns across the codebase.
AST-based code analysis using tree-sitter. Use for parsing code structure, extracting symbols, finding patterns with tree-sitter queries, analyzing complexity, and understanding code architecture. Supports Python, JavaScript, TypeScript, Go, Rust, C, C++, Swift, Java, Kotlin, Julia, and more.
Formatting and linting standards using GTS, ESLint, and Prettier. Use when writing or formatting TypeScript code in this project.
Extracts naming conventions, code style guidelines, and structural patterns specific to the project. Analyzes variable naming, function signatures, file organization, and comment styles.
[Code Quality] Identifies architectural hotspots: high-churn files, coupling issues, layer violations, and dependency tangles. Use to find systemic issues that cause repeated bugs or slow development.
Review code changes (diffs, PRs, patches) and provide structured, actionable feedback on correctness, maintainability, and test coverage. Use when the user asks for a code review, requests feedback on a patch/PR, or wants an assessment of changes.
Validates PopKit code quality using concrete metrics for DRY, coupling, cohesion, and architectural patterns
Detect test smells, overmocking, flaky tests, and coverage issues. Analyze test effectiveness, maintainability, and reliability. Use when reviewing tests or improving test quality.
Python code quality with ruff linter. Fast linting, rule selection, auto-fixing, and configuration. Use when checking Python code quality, enforcing standards, or finding bugs.
ADVISORY validation of code against design principles, accessibility, and best practices that linters cannot fully enforce. Use after linter passes and tests pass to validate design quality. Categorizes findings as Design Debt, Readability Debt, or Polish Opportunities. Does NOT block commits.
Automatic quality control, linting, and static analysis procedures. Use after every code modification to ensure syntax correctness and project standards. Triggers onKeywords: lint, format, check, validate, types, static analysis.
代码简化与优化专家。Use this agent when you need to simplify, optimize, refactor, or clean up code. This agent helps reduce complexity, improve readability, and ensure code follows best practices like YAGNI, KISS, and DRY principles. Supports two modes: analysis mode (provides simplification suggestions) and execution mode (directly modifies code). Works with any programming language. Examples: <example>Context: User wants to simplify complex code. user: "这段代码太复杂了，帮我简化一下" assistant: "Let me analyze and simplify this code using the code-simplifier agent" <commentary>Use code-simplifier to reduce complexity and improve readability.</commentary></example> <example>Context: User wants a code review for optimization opportunities. user: "Review this function for optimization" assistant: "I'll use code-simplifier to identify optimization opportunities and suggest improvements" <commentary>Code-simplifier can analyze and provide optimization recommendations.</commentary></example>
Biome all-in-one formatter and linter for JavaScript, TypeScript, JSX, TSX, JSON, and CSS. Zero-config setup, 15-20x faster than ESLint/Prettier. Use when working with modern JavaScript/TypeScript projects, setting up formatting/linting, or migrating from ESLint+Prettier to a faster alternative.
Expert coding agent for this codebase. Learns from every session to improve code quality, catch edge cases, and apply proven patterns. Use for ANY coding task: writing, debugging, refactoring, testing. Accumulates project knowledge.
This skill should be used when CodeRabbit code review feedback needs to be processed and fixed systematically. Use after running `coderabbit --plain` to automatically save feedback, analyze issues using MCP tools, and implement minimal code fixes with proper planning.
Automatic OWASP security checks on generated code. Use when: any code is generated in the pipeline. Triggers: internal use only.
Runs Static Application Security Testing (SAST) using Semgrep. Scans source code for vulnerabilities, security anti-patterns, and OWASP Top 10 issues. Use when user asks to "run SAST", "scan for vulnerabilities", "static analysis", "code security scan", "静的解析", "脆弱性スキャン".
Analyze codebases for anti-patterns, code smells, and quality issues using ast-grep structural pattern matching. Use when reviewing code quality, identifying technical debt, or performing comprehensive code analysis across JavaScript, TypeScript, Python, Vue, React, or other supported languages.
FTC skill code quality and structure checks. Activates when reviewing PRs, validating skills, or checking plugin compliance with marketplace standards. Use when reviewing pull requests, running local code review, or validating skill structure before submission.
Detects performance anti-patterns like N+1 queries, nested loops, large file operations, and inefficient algorithms. Suggests fast fixes before issues reach production.
