Skill UI

Guides AI coding agents to simplify existing code for clarity while preserving exact behavior, following project conventions and avoiding over-simplification. Useful for refactoring, code review, and cleaning up complex code.

A comprehensive guide for structuring robust Go applications, covering best practices for new projects, from CLI tools to complex microservices and monorepos. It details directory conventions (cmd, internal, pkg), naming rules, adopting 12-Factor App principles, and choosing appropriate architectures (Clean, DDD, Hexagonal).

An advanced, AI-powered agent designed to generate comprehensive and workable unit tests for virtually any programming language (e.g., Python, C#, Java, Go, TypeScript). It operates via a sophisticated multi-agent pipeline (Research → Plan → Implement → Build → Test → Fix → Lint). This ensures generated tests are not only written following project conventions but also compile, pass all tests, and effectively improve code coverage.

Analyzes diverse codebases to auto-detect technology stacks, microservices/monorepo traits, and frontend presence, then documents consistent folder conventions, visualization options, and template guidance to keep large projects organized.

A comprehensive tool that analyzes complex codebases to generate detailed, structured architectural blueprints. It automatically detects and documents the entire technology stack, including primary languages (.NET, Java, Python, etc.), dependencies, versions, licensing, and implementation patterns. The output provides actionable documentation covering coding conventions, usage examples (API, data access), and visual architecture maps, ensuring consistency across guided software development projects.

This skill systematically analyzes an existing codebase to generate seven comprehensive technical documents. It covers the technology stack, architectural patterns, directory structure, coding conventions, external integrations, testing strategies, and technical concerns. It is invaluable for new team members onboarding, conducting project audits, or creating a complete, verifiable system knowledge base.

This guide details production-ready patterns for integrating the Anima SDK into automated design-to-code pipelines. It covers best practices such as implementing a singleton client, managing generation cache, normalizing output for project conventions (e.g., TSX structure, utility classes), and incorporating retry logic for API resilience. Use it when building robust, reusable Anima client wrappers for enterprise applications.

This skill generates a complete React component structure following the industry-standard Container/Presentation pattern. It automatically scaffolds necessary files, including TypeScript, Storybook, and SCSS styles, ensuring adherence to project conventions. Use this when building new, scalable components, allowing you to properly classify whether the component is UI-focused (Presentation) or logic-heavy (Container).

This skill helps developers create highly focused, folder-specific documentation (`CLAUDE.md` and `AGENTS.md`). It systematically analyzes all files in a target directory to extract project purpose, operational constraints, naming conventions, and critical decisions. This deep context ensures that future AI agents (like Claude or Codex) operate with localized knowledge, preventing them from relying on global, generalized instructions and improving their accuracy in that specific domain.

Automatically analyzes unfamiliar codebases to generate a comprehensive, structured onboarding guide. It systematically detects package manifests, framework fingerprints, architecture maps, key entry points, and coding conventions. Ideal for new developers joining a project or setting up a repository for the first time.

This comprehensive guide establishes foundational coding standards and best practices for enhancing code quality, readability, and maintainability across all projects. It covers naming conventions, immutability patterns, error handling, and core design principles like KISS, DRY, and YAGNI, applicable to TypeScript, JavaScript, and React development. Use it when starting a new module or performing code reviews.

Prevents AI style drift in legacy projects by scanning codebases for implicit conventions, resolving conflicts with users, and generating enforceable .ai-style-rules.md files with golden files, naming rules, and DONTs. Ideal for onboarding AI agents to hand-written legacy codebases or extracting unwritten coding rules.