Skill UI

A comprehensive skill for safely restructuring existing Go codebases. It provides behavior-preserving transforms, such as renaming, extracting functions, moving packages, and applying design patterns, while maintaining a safety net via robust testing and structured, atomic commits. Ideal for large-scale maintenance and improving code structure.

This comprehensive guide outlines industry best practices for designing robust and consistent REST APIs. It covers proper resource naming conventions, standardized HTTP method semantics, detailed HTTP status codes (e.g., 201, 404, 422), structured response formats (including pagination and links), and advanced strategies like cursor-based pagination. Essential for building scalable and maintainable APIs.

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.

Implements a robust caching strategy by using the SHA-256 hash of file content as the primary key, instead of the file path. This pattern is essential for high-cost file processing pipelines (like OCR, PDF parsing) because it ensures cache hits survive file renaming or moving, while still automatically invalidating entries when the content changes. It adheres to the Single Responsibility Principle (SRP) by isolating caching logic into a service layer wrapper.

A comprehensive tool designed to audit and validate the structural integrity of a Domain-Driven Design (DDD) codebase. It detects cross-context import violations, checks for violations of aggregate invariant rules, and enforces naming conventions for domain events and repositories. Ideal for CI/CD gates and pre-merge code reviews to prevent architectural decay.

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.

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 tool validates all pending database migrations to ensure correctness, safety, and adherence to best practices. Use it before applying migrations to catch critical issues like broken foreign key references, incomplete rollback logic, unintended destructive operations (e.g., DROP TABLE), and naming convention violations. It checks for rollback completeness, schema integrity, and idempotency.

A comprehensive guide for revising complex multi-agent research papers for top-tier conferences. It emphasizes an 'interaction-first' approach, ensuring the unique contribution is highlighted on the first page. The guide mandates naming solution concepts, making the game legible, and rigorously pairing every major claim with a formal proof, self-play, or deployment experiment.

This guide helps authors structure and write a highly persuasive cover letter for advanced materials submissions. Its purpose is to proactively convince the journal editor that the work represents a genuine, broad-impact scientific advance, rather than just incremental optimization. It focuses on arguing the impact case, naming specific beneficiary communities, and structuring the argument for maximum editorial visibility.

A comprehensive methodological guide for authors positioning their empirical manuscripts within a theoretical debate, particularly for high-level journals like Cognitive Psychology. It teaches authors to move beyond simple literature reviews by identifying a clear theoretical question, naming specific rival models, and demonstrating precisely why existing evidence is insufficient to resolve the conflict. This structure ensures the contribution is framed as an adjudication or a novel model.

This skill reconstructs the full narrative behind a piece of code by analyzing local Git history. Instead of just naming the last author (like git blame), it traces the file's origin, tracks subsequent changes, identifies related companion files, and extracts intent clues from commit messages. Use this when you need to know *why* code exists before refactoring, making risky edits, or understanding complex system behavior.