Skill UI

This tool acts as an expert prompt engineer, guiding users through a systematic discovery process to create highly structured, production-ready prompts for AI assistants like GitHub Copilot. It covers defining persona, specifying complex tasks, managing context variables, integrating tools, and enforcing strict output standards, ensuring the resulting prompt is comprehensive and actionable for advanced AI agents.

This micro-prompt guides AI agents to adopt the mindset of an interactive programmer. It emphasizes using the REPL (Read-Eval-Print Loop) for systematic system exploration and modification, treating the system state as the ultimate source of truth. It also reminds the agent to utilize structural editing tools and maintain todo lists, significantly improving the accuracy and efficiency of the development workflow in live coding environments.

Creates a production-ready Swift MCP Server project using the official MCP Swift SDK, wiring ServiceLifecycle, logging, tool/resource/prompt handlers, and tests so you can quickly spin up a Model Context Protocol server with a standard directory layout.

Generates concise TLDR-format summaries for GitHub Copilot customization files, MCP server docs, or related URLs, ensuring actionable examples and context-aware verbosity for inline or chat view sessions.

Defines TypeSpec API plugin CRUD operations for Microsoft 365 Copilot, wiring GET/POST/PATCH/DELETE routes with parameters, adaptive-card responses, and confirmation prompts to illustrate a complete REST experience.

This structured prompt guides an AI model to proactively analyze a user's question and systematically list all necessary files for accurate answering. It forces the AI to categorize required context into 'Must See' (critical), 'Should See' (helpful), 'Already Have,' and 'Uncertainties.' Ideal for complex coding tasks, debugging, or feature implementation that spans multiple codebase files.

This coach guides users through a progressive mastery of AI, structured across three layers: AI User (prompting), AI-Enhanced Worker (workflow integration), and AI Builder (ML foundations). It uses Socratic questioning, layered scaffolding, and spaced repetition to build genuine, transferable AI literacy, moving beyond simple copy-pasting prompts to deep conceptual understanding of models, transformers, and RAG.

This advanced skill optimizes LLM context usage by implementing strict rules for prompt caching, efficient log pruning, AST-based code inspection, and compact data structuring. It minimizes token consumption and maximizes information density, suitable for highly technical and iterative development workflows.

This expert acts as a Design Systems Lead and Prompt Engineer for creating high-fidelity, consistent UI designs. It transforms vague ideas into structured specifications by enhancing prompts with professional UI/UX terminology, synthesizing design system source files, and managing multi-step generation and editing workflows using the Stitch MCP.

This guide provides comprehensive strategies to significantly reduce spending on the Anthropic Claude API. Key techniques include model routing (selecting the most cost-effective model for the task), prompt caching for repeated prompts, using Message Batches for asynchronous tasks, and implementing real-time spend tracking and budget alerts. Essential for managing large-scale, high-cost AI deployments and optimizing cloud LLM expenditures.

A comprehensive guide detailing common pitfalls and anti-patterns encountered when integrating with the Anthropic Claude API. It highlights crucial differences from other models (e.g., OpenAI), such as the mandatory nature of `max_tokens`, correct handling of system prompts, and precise response parsing techniques. Essential for developers debugging subtle API integration issues and ensuring best practices.

This guide details advanced strategies for optimizing Claude API usage to reduce latency and cost. Techniques covered include prompt caching (significant cost reduction), strategic model selection (Haiku, Sonnet, Opus) based on task complexity, implementing streaming for perceived speed, and managing token limits. Use this guide when building high-performance, cost-efficient LLM applications that interact with large language models.