Skill UI

This skill guides developers on implementing Conversational Language Understanding (CLU) using the official azure-ai-language-conversations Python SDK. It demonstrates how to analyze conversation intent and entities, build advanced NLP features, and integrate language understanding into applications using ConversationAnalysisClient. Best practices emphasize using DefaultAzureCredential and context managers for robust, production-ready code.

This skill provides comprehensive best practices for designing modern and robust React user interfaces. It covers critical patterns for managing asynchronous data, including optimal loading state indicators (skeletons vs. spinners), structured error handling hierarchies, and designing contextual empty states. Essential for improving user experience during data fetching and operations.

This guide details advanced strategies for publishing technical content across major developer platforms like Dev.to and Hashnode. It covers cross-posting best practices, canonical URL management for optimal SEO, and platform-specific optimization (e.g., tagging, content performance) to maximize reach and build authority.

This comprehensive guide provides structured frameworks and best practices for all developer advocacy activities. Use it to plan conference talks (including CFP templates), prepare live coding demos with the 10-3-1 rule, and strategize for podcast appearances and building in public. It covers everything from audience context to presentation polish.

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 guide to designing friction-free developer onboarding experiences. Learn best practices for implementing GitHub OAuth, generating immediate API keys, and utilizing progressive profiling to maximize signup conversion rates while respecting the developer's time. Focuses on moving users from zero to coding as fast as possible.

A portable, framework-agnostic system designed for robust field-side observability in React or React Native apps. It establishes a strongly typed event taxonomy, ensures best-effort, non-blocking data fan-out across multiple analytics providers, and enforces consent gates for all telemetry data. This system complements synthetic testing by capturing real user vitals (LCP, INP, CLS) and behavioral events in production.

This skill guides users on optimizing their GitHub presence for maximum discoverability and professional appeal. It covers best practices for writing compelling README files, structuring comprehensive profile READMEs, and strategically implementing trust badges and marketing elements to enhance project visibility and adoption.

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 professional tool helps developers and researchers identify the optimal AI model for a specific task, such as coding, reasoning, or image recognition. It queries official HuggingFace leaderboards, filters models based on hardware constraints (e.g., available VRAM or RAM), and provides a detailed comparison table including benchmark scores, parameter size, and licensing information.

This skill guides users through building, deploying, and maintaining machine learning applications on Hugging Face Spaces. It provides comprehensive knowledge on selecting the optimal SDK (Gradio, Docker, Static), choosing appropriate hardware resources (ZeroGPU, dedicated GPUs, CPU-basic), and following best practices to ensure ML applications are deployed efficiently and successfully on the platform.

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.