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 initiates a full-quality automated development pipeline for complex tasks spanning multiple files or replacing frameworks. It generates specifications with acceptance criteria, executes code, and verifies results in the background. Ideal for building CLIs, migrations, or API layers. Users provide a goal, and the system handles the entire workflow without manual intervention.

This skill guides users to create new Power Pages code sites (SPA) from scratch or using templates. It supports React, Angular, Vue, and Astro frameworks. The process covers requirements discovery, component planning, design, implementation, validation, and deployment. It ensures production-quality sites with live preview feedback loops and proper Git checkpoints.

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 skill verifies completed work against acceptance criteria using a three-stage pipeline. It performs mechanical checks like linting and testing, semantic reviews for compliance, and multi-model consensus when uncertain. It supports automated fix loops and ensures code quality beyond simple test passing, providing proof of completion.

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.

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 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.

Apply Robert C. Martin-inspired clean code rules to improve code readability, naming conventions, function design, and responsibilities. This skill ensures everyday implementation quality, readable control flow, and testable code by following established best practices for software development.