Skill UI

This tool analyzes SonarCloud quality issues for a specific pull request (PR). It fetches detailed metrics, including severity, issue type, and violation count, and generates a comprehensive, structured report. The output provides an executive summary, identifies top violating files and rules, and delivers a clear, actionable plan to guide developers toward merging high-quality code.

This tool leverages an LLM-as-a-Judge agent to thoroughly evaluate staged Git changes before they are committed. It checks for correctness, completeness, and safety issues, providing a detailed verdict (APPROVE, NEEDS_REVIEW, REJECT) and actionable feedback. It is essential for maintaining high code quality, especially in complex or security-sensitive code sections.

This utility is essential for developers needing to ensure that recent code modifications function as intended. By running the application within a verification environment, users can quickly validate that the changes meet all expected behaviors, maintaining system stability and quality assurance before deployment or merging.

This skill processes raw, report-only optimization observations generated by Caveman tools. It guides the user through an evidence-first, rigorous evaluation process, requiring explicit operator choice, designing a minimal candidate change, and performing a paired baseline evaluation using identical fixed inputs. It ensures that any proposed code change is validated against quality checks and established performance metrics before implementation.

This top-level orchestrator manages the entire lifecycle of developing a feature-rich, native mobile application using Power Apps, React Native, and TypeScript. It governs a multi-stage workflow, covering everything from initial template setup and requirements gathering to data modeling, UI component building, and final deployment, while enforcing strict code gates to ensure high quality.

This skill systematically audits Playwright End-to-End (E2E) test files. It checks for common anti-patterns, adherence to best practices, and identifies coverage gaps across the entire test suite. It provides a detailed score (1-10) and generates actionable reports, including suggested code fixes, significantly improving test reliability and maintainability.

This skill generates high-quality, styled code screenshots by calling the ray.tinte.dev API. It allows users to convert raw code snippets into professional PNG images, supporting multiple programming languages (e.g., Python, JavaScript, TypeScript), customizable themes, and various styling parameters like font size and padding. Use this when the user explicitly requests code images, code snippets as pictures, or mentions 'ray' to visualize code.

skill-doctor grades the performance and quality of an AI agent setup using real, local conversation history (e.g., Claude Code/Codex sessions). It scores skills against defined rubrics for efficiency and code quality, drafts evidence-backed suggestions for improvement, and generates a comprehensive, shareable local report. All processing is strictly local and private.

Automates the conversion of visual designs from Stitch into high-quality, modular React and Vite components. This skill ensures strict adherence to modern architectural standards, including logic isolation via custom hooks, data decoupling, and robust type safety using TypeScript. It manages complex workflows like network fetching, style guide synchronization, and multi-phase validation to generate production-ready frontend code.

This skill provides a comprehensive workflow for creating professional and standardized pull requests (PRs). It enforces critical rules, including mandatory issue linkage to approved items, strict branch naming conventions (e.g., feat/description), adherence to Conventional Commits, and the completion of detailed PR body templates. Use this guide when submitting any code changes to ensure high quality, traceability, and seamless code review.

A comprehensive guide detailing the mandatory steps, naming conventions, and automated checks required for creating branches and opening Pull Requests. This skill ensures all code submissions are linked to approved issues, adhere to conventional commits, and pass rigorous CI requirements before merging, maintaining high code quality and stability.

This skill analyzes excessively large Pull Requests (PRs) that exceed established line budgets (e.g., 400 lines). It automatically splits these massive changes into smaller, focused, and sequential PRs using advanced chaining or stacking strategies. This process is crucial for protecting reviewer focus, ensuring reviews can be completed within a manageable time budget (e.g., ≤ 60 minutes), and maintaining high code quality during complex feature integration.