Skills

Explains complex code in plain language at the requested level of detail. Breaks down algorithms, design patterns, and architecture decisions. Adapts explanation depth from high-level overview to line-by-line walkthrough based on audience.

Generates realistic test data, fixtures, and seed files for databases and APIs. Creates data that respects constraints (foreign keys, unique fields, valid formats) and covers edge cases. Supports JSON, SQL, CSV, and factory patterns.

Generates complete OpenAPI 3.1 specifications from API descriptions, existing code, or route definitions. Includes request/response schemas, authentication, error responses, examples, and server configurations. Produces valid YAML ready for Swagger UI.

Generates .env files, configuration schemas, and environment variable documentation from application requirements. Includes validation rules, default values, required vs optional flags, and example values. Supports multiple environments (dev/staging/prod).

Rewrites vague or technical error messages into clear, actionable user-facing messages. Considers the audience (end-user vs developer), suggests error codes, and provides guidance on what the user can do to resolve the issue.

Generates clear, structured pull request descriptions from code diffs. Includes summary of changes, motivation, testing notes, and reviewer guidance. Follows team conventions and links related issues automatically.

Designs and implements comprehensive error handling for APIs. Covers error response formats (RFC 7807 Problem Details), HTTP status code selection, error logging strategies, retry logic, and client-friendly error messages with proper i18n support.

Performs comprehensive security analysis of code and configurations. Identifies OWASP Top 10 vulnerabilities, insecure patterns, missing input validation, authentication flaws, and secrets exposure. Provides remediation steps with secure code examples.

Translates code between programming languages while preserving logic, idioms, and best practices of the target language. Handles differences in type systems, error handling, concurrency models, and standard library APIs. Produces idiomatic target code, not line-by-line transliteration.

Generates clear, conventional commit messages from code diffs. Follows Conventional Commits specification with appropriate type prefixes, scopes, and descriptions. Handles breaking changes, multi-file changes, and produces both concise subjects and detailed bodies.

Revisión automatizada de código que ofrece comentarios aplicables sobre calidad, posibles errores, problemas de rendimiento, vulnerabilidades de seguridad e incumplimientos de estilo. Analiza los cambios con el rigor de un ingeniero sénior y aporta sugerencias concretas con ejemplos de código.

Generates comprehensive documentation from code including API references, README files, architecture decision records (ADRs), inline comments, and user guides. Adapts tone and detail level to the target audience (developers, end-users, stakeholders).

Guides systematic code refactoring while preserving exact behavior. Identifies code smells, suggests appropriate refactoring patterns, and executes transformations incrementally with verification at each step. Follows Chesterton's Fence principle — understands why code exists before changing it.

Diseña API RESTful y GraphQL siguiendo principios de contrato primero. Abarca la estructura de endpoints, los esquemas de solicitudes y respuestas, la gestión de errores, el versionado, la paginación, la autenticación y los límites de uso. Produce especificaciones OpenAPI/Swagger y scaffolding de implementación.

Generates optimized SQL queries from natural language descriptions. Supports multiple dialects (PostgreSQL, MySQL, SQLite, SQL Server), handles complex joins, subqueries, window functions, and CTEs. Includes query explanation and performance optimization hints.

Designs, optimizes, and iterates on prompts for LLM applications. Covers system prompt design, few-shot examples, chain-of-thought reasoning, output formatting, and prompt testing strategies. Helps build reliable AI-powered features.

Analyzes datasets to extract insights, identify patterns, and generate visualizations. Supports exploratory data analysis (EDA), statistical testing, trend detection, and report generation. Works with CSV, JSON, and database outputs.

Identifies and resolves performance bottlenecks in code and systems. Covers algorithmic complexity analysis, memory optimization, caching strategies, database query tuning, and frontend performance (Core Web Vitals). Follows a measure-first approach.

Systematic debugging workflow that helps identify, isolate, and fix bugs. Follows a structured approach: reproduce, localize, reduce, fix, guard. Analyzes error messages, stack traces, and logs to pinpoint root causes rather than symptoms.

Generates comprehensive unit tests for existing code, covering happy paths, edge cases, error conditions, and boundary values. Follows testing best practices including the test pyramid, DAMP over DRY, and the Arrange-Act-Assert pattern. Adapts to the project's existing test framework and conventions.

Designs and implements CI/CD pipelines for various platforms (GitHub Actions, GitLab CI, Jenkins, CircleCI). Covers build, test, lint, security scan, deploy stages with proper caching, parallelization, and environment management.