Skill UI

This guide details best practices for implementing robust, type-safe GraphQL queries and mutations. It covers using separate .gql files, running code generation tools, and implementing mandatory error handling (onError) and loading states. Use this when building complex, reliable data fetching layers with Apollo Client.

A comprehensive skill for generating production-grade, robust automation scripts and end-to-end tests using Selenium WebDriver. Supports multiple languages (Java, Python, JS, C#, etc.) and execution environments, including local machines and TestMu AI cloud for cross-browser compatibility. It guides users on best practices like explicit waits, Page Object Model (POM), and stable locator strategies, acting as a senior QA automation architect.

This skill helps generate comprehensive and structured TestNG test cases for Java. It supports advanced testing features crucial for enterprise automation, including method grouping, data-driven testing using @DataProvider, parallel execution setup, XML suite configuration, and custom listeners. Use it when building robust, scalable, and maintainable automated test suites.

This template generates structured, multi-frame video scripts compatible with Remotion and Hyperframes. It guides the creation of cinematic, high-impact videos, ensuring each frame conveys a single, strong concept. The output includes necessary metadata for automated playback, transitions, and timing, making the script ready for professional video rendering into MP4.

A comprehensive, minimal landing page template designed for product pre-launch or early access sign-ups. It features crucial elements like email capture forms, brand branding, hero sections, and sophisticated decorative overlays (meshes, noise). Perfect for maximizing early interest and lead generation.

This skill acts as a critical reality-check layer before code generation. It ensures that every function call, import, method, and variable the agent uses is provably available in the user's specific installed environment and version. It prevents runtime bugs caused by hallucinated or improperly assumed APIs, making the generated code robust and reliable.

WGM is a robust, agentic methodology designed to transform vague or rough requirements into working, validated software. It enforces a disciplined workflow across six stages: deep alignment (Grill), structured planning, deterministic iterative building (Loop), mandatory holdout-scenario judging, and final documentation audits. It ensures that the resulting code converges against rigorously tested acceptance criteria rather than relying on single-pass generation.

This skill builds an active voice profile by extracting stylistic patterns, rhythms, and signatures from genuine source materials—such as posts, essays, and site copy. Instead of relying on generic AI tropes, it creates a reusable VOICE PROFILE block, ensuring consistent brand messaging across all content, social media, and outreach campaigns.

This tool formats prompts for various Large Language Model (LLM) providers, including Anthropic, OpenAI, Gemini, and Ollama. It is essential for building robust Retrieval-Augmented Generation (RAG) pipelines. Users can format messages, create HNSW indexes from documents, route relevant context, and manage provider status, ensuring compatibility across different AI ecosystems.

This tool provides comprehensive functionality for generating, updating, and maintaining project documentation. It excels at detecting 'documentation drift' by comparing the current codebase against existing documentation, flagging any inconsistencies. Users can scope generation to specific APIs or the entire project, and set up recurring maintenance schedules using cron triggers.

This skill fetches the latest, live documentation and code examples for specific libraries and frameworks (e.g., Next.js, React, Prisma). Instead of relying on static or outdated training data, it utilizes the Context7 MCP to provide accurate, up-to-date answers for setup questions, API references, and code generation based on current best practices.

A formal framework designed for evaluating AI-assisted code generation sessions. It implements Development Oriented Evals (EDD) principles, allowing users to define capability and regression criteria. The system supports multiple evaluation types—code-based, model-based, and human review—and tracks reliability using advanced metrics like pass@k, ensuring robust quality control for AI agents.