Skill UI

This skill executes the final two phases of the SPARC methodology: Refinement and Completion. It systematically reviews the implemented feature through rigorous code reviews, expands test coverage (unit, integration, edge cases), validates performance, generates a traceability matrix, creates comprehensive documentation, and confirms deployment readiness. It ensures the feature is robust, tested, and production-ready.

A comprehensive CI/CD quality gate checklist for Spring Boot projects. It guides developers through mandatory steps including building, static analysis (SpotBugs, PMD), running unit/integration tests, measuring code coverage, performing security scans (OWASP), and reviewing diffs before merging or deploying.

This comprehensive guide demonstrates how to achieve highly testable Swift code using protocol-based dependency injection. By abstracting external concerns—such as file systems, networking, and external APIs—into small, focused protocols, developers can write deterministic unit tests that mock side effects and error paths, ensuring robust architecture across different environments (App, Test, Preview).

A comprehensive, multi-stage skill designed to simulate a professional Continuous Integration (CI) pipeline. It guides users through critical quality gates, including build verification, type checking, linting, running test suites (with coverage checks), security scanning for secrets, and final diff review. Use this skill before creating a Pull Request or finalizing any significant feature to ensure code robustness and adherence to best practices.

An advanced, secure skill leveraging OpenAI Codex and GPT models for end-to-end software development tasks. It supports autonomous code implementation, complex bug fixing, architectural analysis, and rigorous code/plan review. Operating in a controlled sandbox environment, it adheres to least privilege principles, allowing users to delegate entire coding workflows, from feature development to security auditing, without manual intervention on every step. Ideal for developers needing full-cycle automation assistance.

A sophisticated, multi-agent workflow designed for comprehensive code reviews of GitHub Pull Requests. It performs deep analysis by simulating multiple specialized perspectives, checking PR eligibility (handling draft/merged status and follow-ups), and managing large or complex diffs. The skill synthesizes high-signal, actionable feedback by integrating parallel reviewers' findings with historical PR discussions, ensuring robust code quality before merging.

This comprehensive guide outlines an opinionated set of conventions for building robust Ruby on Rails applications. It covers core architectural principles, including rich models, state-as-records, minimal dependencies, and best practices for CRUD operations and testing. It is mandatory to consult these guidelines whenever designing, writing, reviewing, or refactoring any Rails code.

This tool generates a detailed, local 'receipt' or impact report of all Claude Code activity recorded in local session transcripts. It cross-references this data with your local Git history to provide a quantifiable measure of effort and project contribution. Ideal for performance reviews, budget justifications, or self-auditing usage.

A comprehensive security auditing tool designed to analyze codebases for vulnerabilities. It provides a structured workflow to scan entire repositories, review specific changes (like PR diffs or single commits), and suggest verified patch files. Findings are challenged by independent agents, ensuring that suggested fixes are targeted and require manual review before application.

This comprehensive guide provides authors with best practices for preparing supplementary artifacts for AAAI submissions. It outlines how to structure technical appendices, package reproducible code and data ZIPs, maintain strict anonymity (by removing author metadata and paths), and maximize the evidence for reproducibility. The goal is to ensure that external reviewers can easily verify the research claims without specialized or private tools.

This guide provides comprehensive instructions for packaging multiagent system (MARL) and game theory evidence for academic submissions, particularly for conferences like AAMAS. It emphasizes that the artifact must be inspectable, containing not just a trained model, but the game definition, environment code, opponent sets, and interaction protocols, ensuring rigorous reproducibility and reviewability.

Provides comprehensive guidelines for authors submitting papers to Acta Automatica Sinica (AAS). It details how to organize and present anonymous, reproducible code, data, and experimental artifacts to enhance the credibility and rigor of research in control, automation, and pattern recognition, supporting the double-blind review process.