Skill UI

A comprehensive guide detailing best practices for writing clean, maintainable, and type-safe Java code, specifically tailored for Spring Boot services. It covers naming conventions, prioritizing immutability, proper use of Optional, stream API best practices, and modern project structuring.

This skill acts as an advanced orchestration protocol for reviewing GitHub pull requests. It is stack-agnostic, meaning it does not enforce its own rules. Instead, it dynamically discovers the project's existing standards—such as the test runner, architectural conventions, and requirement specifications—before running six specialized subagents in parallel. It provides a comprehensive, context-aware review and summary directly on GitHub.

This tool analyzes specified source code files or modules to automatically generate comprehensive and structured test suites. It identifies testable units (functions, classes, methods), adheres to project conventions (like AAA pattern), and ensures high coverage for critical scenarios, including happy paths, boundary edge cases, and error handling. It supports popular frameworks such as Jest, Vitest, and pytest.

This advanced skill initializes the Software Development Documentation (SDD) context within a project. It detects the entire technical stack, conventions, testing tools, and persistence mode across all discovered projects. It then structures and persists this comprehensive context, including building a skill registry and determining the strict TDD status, ensuring a robust starting point for development.

Generate tailored AGENTS.md and companion rules by analyzing codebases. Supports full, minimal, update, and dry-run modes with automatic package manager detection, monorepo support, and command validation. Creates living documentation matching your project's actual toolchain including setup commands, verification cycles, conventions, and architecture rules.

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.

Framework-agnostic long-term project memory for AI agents. Persists architecture decisions, conventions, and context as plain Markdown files in .lore/, enabling any agent to record, recall, audit, sync, and query project knowledge.

This skill generates new UI components adhering to the StyleSeed design conventions. It ensures consistency with Tailwind v4, CVA, and accessibility standards. Use it for creating reusable primitives or patterns within React projects. Do not use for full-page scaffolding or non-StyleSeed projects.