Skill UI

A constraint-based protocol designed to professionalize existing code by normalizing comments and performing safe, non-semantic cleanup. This skill converts informal, outdated, or missing documentation into clear, concise, and professional standards without altering the core logic or behavior. Ideal for preparing code for code review, open-sourcing, or formal documentation.

A sophisticated presentation template designed for technical content, mimicking the clean, professional aesthetic of GitHub blogs or product changelogs. It features a dark GitHub-dark background, vibrant purple and blue radial ambient lighting, a multi-color gradient title, and dedicated GitHub-style code blocks. Perfect for tech talks, deep-dive tutorials, and developer documentation.

This skill provides a comprehensive, evidence-based inventory and overlap audit workflow for enterprise automation. It helps users determine which jobs, hooks, connectors, or wrappers are currently live, broken, redundant, or missing before making critical changes, ensuring reliable system architecture and clean code practices.

This utility provides garbage collection for Claude Code configurations. It periodically scans various directories—including skills, memory files, hooks, permissions, and caches—to identify orphaned, stale, or redundant configuration items. Crucially, the process is designed to be non-destructive, always requiring explicit human confirmation for every deletion to ensure a safe, audited cleanup process.

A comprehensive, library-agnostic checklist covering best practices for Flutter and Dart applications. It guides developers on architectural principles, state management patterns (BLoC, Riverpod, etc.), Dart idioms, performance optimization, and clean code principles, making it invaluable for code reviews.

A comprehensive guide to professional Git version control. It covers best practices for branching strategies (GitHub Flow, GitFlow, Trunk-Based), writing standardized commit messages using Conventional Commits, managing merges vs. rebases, and defining effective Pull Request workflows. Ideal for any development team looking to improve code collaboration and maintain a clean, traceable history.

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 manages the secure transition of private internal projects to public open-source releases. It executes a three-stage pipeline: Forking (removing secrets), Sanitizing (verifying clean code), and Packaging (generating required documentation like README, setup.sh, and CLAUDE.md). It ensures code safety and readiness before public deployment.

This skill automates the entire GitHub Pull Request (PR) review cycle, specifically targeting Copilot Code Review comments. It runs a robust loop: automatically triggering reviews, triaging open threads, dispatching parallel fixes that adhere to repo conventions, committing changes, and resolving comments. It continues iterating until the PR is deemed clean and ready for merge, minimizing manual effort and ensuring high code quality.

This guide outlines the creation of a complete, verifiable research package for economic policy manuscripts. It ensures that all empirical, experimental, or simulation results are fully reproducible by providing raw data guides, cleaning scripts, analysis code, and a single 'master' script that runs end-to-end. This process is critical for academic acceptance, allowing reviewers and discussants to independently verify every figure, table, and key number reported in the paper.

This comprehensive guide details the strict replication and data deposit policies required by the Journal of International Economics (JIE). It ensures that all components—including raw data, cleaning scripts, structural solver code, and master scripts—are properly deposited in a secure repository (like Mendeley Data) before publication acceptance, guaranteeing full research reproducibility.

This specialized tool analyzes Git diffs against a stated development intent to detect scope creep. It identifies overly broad pull requests, unrelated files, dependency additions, public API renames, or configuration changes. It guides users on whether changes should be kept, split into separate commits, or if the connection requires explicit justification, ensuring clean and focused code merges.