Skill UI

A specialist tool designed to generate, format, and validate comprehensive technical documentation. It supports multiple standards, including Google/NumPy docstrings (for Python), JSDoc (for TypeScript), and full OpenAPI/Swagger specifications. Key features include automated detection of code languages and frameworks, robust validation of code examples (using tools like doctest and OpenAPI linters), and generating structured user guides or site configurations.

A mandatory workflow for ensuring high code quality across multiple ecosystems, including Node.js/TypeScript and Python. This pipeline runs static analysis tools—such as linters, type checkers (MyPy, tsc), and security scanners (Bandit)—after every code change. It mandates that no task is considered 'done' or committed until all validation checks pass, resolving style, syntax, and type mismatches.

Aggregates Uncle Bob’s Clean Code, Architecture, Coder, Agile, and design-pattern guidance to evaluate reviews, refactors, and architecture decisions, and to surface professionalism heuristics without replacing your linter or formatter.

A comprehensive guide to idiomatic Go programming style and code clarity. This skill addresses best practices for line breaking, variable declaration intent, control flow structure (e.g., early returns, switch usage), and function design principles. Use it when writing, reviewing, or establishing project-wide coding standards to ensure maximum readability and maintainability.

A comprehensive guide to achieving high code quality in Go projects using `golangci-lint`. This skill covers the entire development lifecycle, including setting up the essential `.golangci.yml` configuration, running automated linters (style, security, complexity), automatically fixing issues, interpreting detailed lint output, and correctly suppressing false positives using `//nolint` directives. Ideal for enforcing best practices in CI/CD pipelines and maintaining legacy codebases.

Defines mandatory contribution guidance for repository work, insisting contributors locate workflow documentation, follow branch/commit/PR templates, and run required linters or tests before submitting changes, without executing forbidden scripts.

This guideline establishes a strict principle: never claim completion, success, or fix status without fresh, verifiable evidence. It mandates running explicit verification commands (like tests or linters) and presenting the full output and exit codes to support any assertion of work completion. It helps prevent overconfidence and reliance on assumption or proxy reports.

This advanced code reviewer analyzes your codebase through the principles of 12 classic software engineering books (e.g., Clean Code, DDIA, Mythical Man-Month). It detects conceptual flaws, design smells, tight coupling, missing abstractions, and architectural risks—issues that standard linters and basic AI tools often miss. Essential for major refactoring and pre-design validation.

Logic Lens is an advanced AI skill that performs deep, formal logic-driven code review. Unlike standard linters, it analyzes code for complex issues like race conditions, security vulnerabilities (SQL injection, XSS), type safety flaws, and algorithmic errors across 9 critical risk categories. It is ideal for pre-merge PR reviews, auditing sensitive code paths, and refactoring complex business logic.

An internal tool designed to validate candidate meta-skills (SKILL.md) before they are registered in the system. It performs comprehensive linting checks, including structural validation (G1: parsing, reference checks, XML escaping) and a scheduler dry-run (G2) using stub executors, ensuring the skill is deterministic and functional. Output diagnostics in JSON format.

A fast, grep-based linter that checks for common design system violations in CSS and component code, such as hardcoded colors, raw pixel values, and deprecated syntax. It provides immediate automated feedback to ensure code adheres to established design tokens and best practices.

AI code reviewer grounded in 12 classic software engineering books. It detects design smells, tight coupling, missing abstractions, stability gaps and data-integrity risks that ordinary linters and AI tools typically miss.