Total 54,043 skills, Code Quality has 2436 skills
Showing 12 of 2436 skills
Effectue des revues de code complètes des merge requests GitLab, analysant la qualité du code, la sécurité, les performances et les bonnes pratiques. À utiliser quand l'utilisateur dit « review » ou « code review » ou demande de revoir des merge requests ou d'analyser les changements d'une branche avant fusion.
Captures quality metrics baseline (tests, coverage, type errors, linting, dead code) by running quality gates and storing results in memory for regression detection. Use at feature start, before refactor work, or after major changes to establish baseline. Triggers on "capture baseline", "establish baseline", or PROACTIVELY at start of any feature/refactor work. Works with pytest output, pyright errors, ruff warnings, vulture results, and memory MCP server for baseline storage.
Apply language-agnostic naming conventions using the A/HC/LC pattern. Use when naming variables, functions, or reviewing code for naming consistency.
Anti-patterns for CLI development. Do NOT use commander, inquirer.js, yargs, or other CLI libraries in Constructive projects. Use inquirerer instead. Triggers on "commander", "inquirer.js", "yargs", "CLI library", or when reviewing CLI code.
Find orphan functions, dangling imports, and dead code via GitNexus CLI (npx gitnexus@latest). CLI ONLY - NO MCP server exists, never use readMcpResource with gitnexus:// URIs. TRIGGERS - dead code, orphan functions, unused imports, dangling references, unreachable code.
Scans code against 17 named design smells and produces a structured diagnostic report. Use when reviewing a PR for design quality, evaluating unfamiliar code against a comprehensive checklist or when the user asks for a red flags scan. Not for diagnosing why code feels complex (use complexity-recognition) or evaluating whether a PR maintains design trajectory (use code-evolution).
Run after making Docyrus API changes to catch bugs, performance issues, and code quality problems. Use when implementing or modifying code that uses Docyrus collection hooks (.list, .get, .create, .update, .delete), direct RestApiClient calls, query payloads with filters/calculations/formulas/childQueries/pivots, or TanStack Query integration with Docyrus data sources. Triggers on tasks involving Docyrus API logic, data fetching, mutations, or query payload construction.
Python coding standards with automatic version detection. Use when writing, reviewing, or refactoring Python to ensure adherence to LBYL exception handling patterns, modern type syntax (list[str], str | None), pathlib operations, ABC-based interfaces, absolute imports, and explicit error boundaries at CLI level. Also provides production-tested code smell patterns from Dagster Labs for API design, parameter complexity, and code organization. Essential for maintaining erk's dignified Python standards.
Comprehensive code investigation and audit tool. Discovers all project features, then dispatches parallel subagents to analyze issues, risks, dead code, missing functionality, and redundancies. Produces a prioritized risk report. Use this skill when the user asks to "investigate code", "audit project", "find risks", "check code quality", "analyze codebase", "what's wrong with this code", "project health check", "code review entire project", "find dead code", "find redundant code", or any request for a thorough codebase analysis.
Retrieve code review results from DeepSource — issues, vulnerabilities, report cards, and analysis runs. Use when asked about code quality, security findings, dependency CVEs, coverage metrics, or analysis status.
Skill for creating custom lint rules by leveraging the existing linter ecosystems of various programming languages. This is a linter designed for AI Agents rather than humans, and its error messages function as correction instruction prompts for AI. Create custom rules in the `lints/` directory using standard methods for each language, including Rust (dylint), TypeScript/JavaScript (ESLint), Python (pylint), Go (golangci-lint), etc. Use this skill in the following scenarios: (1) When you want AI to enforce project-specific coding rules; (2) When you want to create lint rules that output AI-readable correction instructions when violations occur; (3) When you want to enforce naming conventions, structural patterns, and consistency rules through AI-driven linting. Triggers: "Create a linter rule", "Add a lint rule", "Enforce this pattern", "AI linter", "Custom lint", "Code rules", "Naming rules", "Structural rules", "create a linter rule", "add a lint rule", "enforce this pattern", "AI linter".
Coding conventions enforcement agent. Auto-invoked when writing new code, reviewing code quality, adding headers, or checking documentation compliance across Python, TypeScript/JavaScript, and C#/.NET.