Skill UI

A comprehensive guide detailing Apple's Human Interface Guidelines (HIG) for implementing essential UI status components. Covers best practices for progress indicators (determinate vs. indeterminate), status bars, and activity rings across iOS and watchOS. Essential for developers ensuring native, platform-compliant, and user-friendly status feedback.

A comprehensive guide to the Apple Human Interface Guidelines (HIG), detailing best practices for designing seamless and intuitive user experiences across various interaction scenarios. It covers key principles such as minimizing modality, providing clear feedback, handling data entry, progressive onboarding, and managing complex features like sharing and drag-and-drop, ensuring your application feels native and polished.

This sophisticated skill automates the entire Pull Request (PR) quality assurance lifecycle. It systematically fetches and categorizes CI check failures and detailed review feedback using the LOGAF priority scale. The workflow guides users through root cause analysis, proposing minimal, targeted fixes, replying to reviewers, and committing updates iteratively until all checks pass, ensuring high code quality before merging.

This setup configures a comprehensive local development workflow for integrating with the Juicebox API. It provides a fast feedback loop by allowing developers to mock profile searches and candidate enrichment data. Users can easily toggle between mock mode for rapid iteration and sandbox mode for validating against the real API, ensuring efficient development without consuming live API credits.

A comprehensive workflow for implementing robust LLM evaluation using Langfuse. This skill guides users through key processes including scoring traces (numeric, categorical, boolean), collecting user feedback, managing versioned prompts, creating structured datasets, running automated experiments, and implementing LLM-as-a-Judge patterns for advanced quality assurance and system benchmarking.

LibAFL is a modular fuzzing library in Rust that exposes advanced mutators, feedback, objectives, and schedulers so you can build bespoke fuzzers or use it as a drop-in replacement for libFuzzer when targeting non-standard architectures or research experiments.

Analyzes transcripts and recordings to spot conflict avoidance, dominance, filler words, and listening lapses, delivering timestamped feedback and trend comparisons for leaders.

Create structured moodboards that synthesize inspiration from trend research or site analysis, refining colors, typography, and patterns through iterative user feedback before implementation.

Walks through n8n validation feedback, from errors to warnings and suggestions, and explains profiles and loops so you can interpret results, fix config mistakes, and deploy reliably.

This guide establishes a comprehensive and rapid development loop for Obsidian plugins. It covers the entire workflow, from setup (cloning templates, symlinking) to advanced features, including running esbuild in watch mode, achieving hot-reloading (via Ctrl+R or automatic methods), deep debugging with Chrome DevTools, and running unit tests with vitest. Essential for developers seeking fast feedback and reliable plugin iteration.

Guidelines for responding to code review feedback with technical rigor: read every comment, restate requirements, verify against the codebase, ask clarifying questions, push back when technically needed, and implement fixes one at a time with testing.

This workflow provides an AI-powered platform for immersive conversation practice. It uses advanced language models (like GPT-4o) and real-time speech APIs to simulate real-life scenarios (e.g., ordering food, navigating). Users receive immediate, detailed feedback on pronunciation (phoneme level), grammar corrections, and vocabulary notes, making it an ideal core module for building language fluency and simulating professional tutoring experiences.