Skill UI

Transforms natural-language prompts into Excalidraw JSON diagrams, supporting flowcharts, relationships, mind maps, architecture, DFD, swimlane, class, sequence, and ER views; ideal when users ask to visualize processes or system structures and need editable Excalidraw files.

A comprehensive guide to building production-grade evaluation systems for Large Language Models (LLMs). This skill covers advanced methodologies, including Direct Scoring and Pairwise Comparison, alongside critical techniques for mitigating systematic biases (e.g., Position Bias, Length Bias). Learn how to select appropriate metrics and structure prompts to ensure reliable, consistent, and objective quality assessment across various AI applications.

Performs static security analysis on GitHub Actions workflows that incorporate AI coding agents (e.g., Claude, Gemini, OpenAI). This skill helps identify critical data flow vulnerabilities, such as where attacker-controlled input can reach AI agent prompts or internal systems via CI/CD pipelines, going beyond simple expression checks.

Transforms vague UI ideas into structured, Stitch-optimized prompts by adding platform context, UI/UX keywords, design system cues, and detailed page structure guidance so generated interfaces stay consistent and polished.

Provides architecture design support including ADR templates, system design checklists, scalability assessment prompts, architecture patterns, and API guidance so engineers can document decisions and ensure reliability during technical design phases.

This methodology converts verbose, natural language system instructions into an atomic, structured-label format (e.g., trigger:, action:). By structuring rules, it bypasses the limitations of natural language processing, ensuring LLMs allocate full attention to every constraint, thereby maximizing compliance, improving reliability, and reducing token usage.

This tool is designed to help optimize and refine prompts used with Large Language Models (LLMs). By structuring inputs, it ensures that the instructions given to AI systems are clear, comprehensive, and contextually rich, resulting in significantly higher quality and relevance of the generated output. Useful for developers, writers, and researchers.

This skill provides expert guidance on treating prompts as first-class infrastructure. It covers prompt registry design, systematic versioning, A/B testing, and building comprehensive evaluation pipelines. It ensures that prompt changes are rigorously tested and deployed safely, preventing quality regressions in production AI features at scale.

This skill provides a comprehensive framework for optimizing, improving, and debugging Large Language Model (LLM) prompts. It leverages production trace data, system evaluations, and human annotations to extract prompts from spans. Users can run a data-driven optimization loop, ensuring the AI responses meet desired quality standards. Ideal for prompt tuning, system prompt refinement, and improving overall AI output quality.

Generates Excalidraw diagrams from natural language prompts, covering flowcharts, relationship maps, mind maps, architecture, data flow, swimlane, class, sequence, and ER diagrams for communicating processes and systems.

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 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.