Total 44,061 skills, Code Quality has 2066 skills
Showing 12 of 2066 skills
Use this skill when conducting or improving code reviews. Provides structured review processes, conventional comments patterns, language-specific checklists, and feedback templates. Ensures consistent, constructive, and thorough code reviews across teams.
Expert coding guide for OpenHarmony C++ development. Use this skill when writing, refactoring, or reviewing C++ code for OpenHarmony projects. It enforces strict project-specific conventions (naming, formatting, headers) and critical security requirements (input validation, memory safety).
Comprehensive ESLint agent for JavaScript/TypeScript code quality. Use when setting up ESLint, configuring linting rules, analyzing code for issues, fixing violations, or integrating ESLint into development workflows. Triggers on requests involving code quality, linting, static analysis, or ESLint configuration for JavaScript, TypeScript, React, or Node.js projects.
Enforce language-specific coding standards (Python/TS/JS/Go/Rust/C/C++) + PR/commit conventions.
Systematic debugging methodology emphasizing root cause analysis over quick fixes
ESLint JavaScript linter with plugins. Use for code quality.
Architecture analysis, violation detection, and pattern validation. USE WHEN: reviewing code architecture, identifying violations, verifying patterns, updating technical documentation. Reference: docs/02-architecture/ARCHITECTURE.md Examples: <example> Context: User wants to check if code follows architecture. user: "Analyze if the payment module follows our architecture" assistant: "I'll use architecture-analyzer to review against ARCHITECTURE.md." <commentary>Architectural review is architecture-analyzer specialty.</commentary> </example> <example> Context: Need to identify technical debt. user: "Find architectural violations in the services layer" assistant: "I'll use architecture-analyzer to scan for violations." <commentary>Violation detection is architecture-analyzer responsibility.</commentary> </example>
Analyzes Java code against industry best practices and evaluates design principles including SOLID, exception handling, thread safety, and resource management. Reviews naming conventions, Stream API usage, Optional patterns, and general code quality. Use when reviewing Java files, checking code quality, evaluating exception handling, or auditing resource management.
Systematic resolution of pyright/mypy type errors with categorization and fix templates. Use when pyright fails, type errors are reported, adding type annotations, or enforcing type safety. Analyzes Python type errors, categorizes them (missing annotations, incorrect types, generic issues, Optional/None handling), and applies fix patterns. Works with .py files and pyright output.
Verifies that implemented code is actually integrated into the system and executes at runtime, preventing "done but not integrated" failures. Use when marking features complete, before moving ADRs to completed status, after implementing new modules/nodes/services, or when claiming "feature works". Triggers on "verify implementation", "is this integrated", "check if code is wired", "prove it runs", or before declaring work complete. Works with Python modules, LangGraph nodes, CLI commands, API endpoints, and service classes. Enforces Creation-Connection-Verification (CCV) principle.
Enforces the discipline of thinking about tests, features, and maintainability BEFORE writing implementation code. Use when starting new classes/methods, refactoring existing code, or when asked to "think about tests first", "design for testability", "what tests do I need", "test-first approach", or "TDD thinking". Promotes simple, maintainable designs by considering testability upfront. Works with any codebase requiring test coverage and quality standards.
Provides reflective questioning framework to challenge assumptions about work completeness, catching incomplete implementations before they're marked "done". Use before claiming features complete, before moving ADRs to completed status, during self-review, or when declaring work finished. Triggers on "is this really done", "self-review my work", "challenge my assumptions", "verify completeness", or proactively before marking tasks complete. Works with any type of implementation work. Enforces critical thinking about integration, testing, and execution proof.