Skill UI

This skill specializes in transforming theoretical threat models, risk assessments, and business context into concrete, actionable security requirements. It is essential for writing detailed security user stories, defining robust acceptance criteria, building comprehensive security test cases, and ensuring compliance requirement mapping. Use it when bridging the gap between identifying a vulnerability and specifying how the system must be secured.

This skill provides methods for reliably extracting typed, validated, and structured data from Large Language Models (LLMs), moving beyond simple text parsing. It covers best practices across major APIs (OpenAI's response_format, Anthropic's tool_use, Google's responseSchema) and local models. Key workflows include defining schemas (Pydantic/Zod), implementing constrained decoding, and building robust retry loops for production-grade data pipelines.

This guide provides advanced patterns for composing robust, multi-step Large Language Model (LLM) workflows using LangChain 1.0 core runnables. Learn to implement parallel retrievals (RunnableParallel), conditional routing (RunnableBranch), and state management. It also addresses common pitfalls like silent KeyErrors due to mismatched dictionary shapes, providing solutions using debug probes and type annotations for reliable RAG, classification, and extraction pipelines.

A comprehensive tool for searching the Model Context Protocol (MCP) across various GitHub locations, including PRs, issues, and discussions. It allows users to track the full lifecycle of protocol specifications, from initial proposals to final merged changes. Furthermore, it facilitates deep dives into pull request reviews and comments, enabling the extraction of key maintainer rationales and design intent for accurate technical understanding.