Skill UI

This skill performs a comprehensive usability audit and heuristic evaluation of interactive user interfaces (UIs). It goes beyond mere visual aesthetics, assessing whether a system is intuitive, easy to learn, and functional for a first-time user. Use it when auditing complex tools, editors, or apps to ensure the user can complete primary tasks without needing extensive instructions or guidance.

This foundational skill guides the establishment and maintenance of a comprehensive developer audience context. It helps map out detailed developer personas, identify core pain points, analyze current alternatives, and determine key technical differentiators. By structuring this context across 10 critical sections, it ensures all subsequent developer marketing materials are highly targeted and speak the exact language of the target user.

A comprehensive skill for systematically monitoring developer conversations across technical platforms like GitHub, Reddit, and Stack Overflow. It helps track brand mentions, competitor positioning, and unsolved technical problems to gather unfiltered product feedback and high-intent leads.

This comprehensive guide provides strategies for optimizing the developer onboarding experience. Learn how to get developers to their "first value" (Time-to-First-Value) as quickly as possible using optimized quickstarts, tutorials, and structured sample apps. It covers measuring TTFV, identifying common blockers (like hidden prerequisites), and determining the best mix of interactive vs. static documentation to maximize product adoption rates.

A comprehensive guide on designing, building, and optimizing interactive developer playgrounds. Learn principles covering instant gratification, progressive complexity, and zero friction to maximize developer engagement, demonstrate core value, and convert curious visitors into active users.

This comprehensive skill guides the creation of high-quality technical content specifically tailored for developers. It covers the entire devrel content lifecycle: from researching topic validation (community signals, competitor gaps) to selecting the ideal content type (tutorial, guide, case study). The framework emphasizes deep technical accuracy, structured outlines (Hook, Meat, CTA), and writing runnable, copy-paste ready code examples to build trust and drive adoption.

Utilize this skill to assess the health, adoption, and performance metrics of published EAS Updates directly via the CLI. It reports critical data points including crash rates, unique user counts, install volumes, payload size, and the split between embedded and OTA users, making it essential for post-publish monitoring and regression detection.

EAS Observe is a comprehensive tool for monitoring performance metrics in production Expo applications. It tracks critical performance data, including app startup time, navigation performance across routes, and custom user events. Developers can integrate it into their Expo project using the `expo-observe` package and query detailed reports via the EAS CLI, ensuring optimal user experience and stability.

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.

This skill provides comprehensive access to the Hugging Face Hub ecosystem via MCP server tools. Users can search for state-of-the-art models, datasets, and research papers, retrieve detailed repository information, fetch documentation, run compute jobs on GPU/CPU, and interact with Gradio Spaces as fully functional AI tools. Ideal for ML development, research, and application prototyping.

A comprehensive guide for developers building AI demos and GPU compute tasks on Hugging Face Spaces using ZeroGPU hardware. It covers best practices for writing and reviewing code that utilizes the `@spaces.GPU` decorator, managing runtime duration and quota, and understanding the specific CUDA availability model and process isolation rules required for reliable ML deployment.

This skill is designed to accurately convert complex, Hugo-managed documentation sites and content into standard Markdown format. It goes beyond generic knowledge by inspecting the entire local Hugo repository structure, including `hugo.toml`, custom shortcodes, layouts, and content files. It preserves the original content's semantic meaning while materializing Hugo-specific constructs into clean Markdown.