Skill UI

This skill guides the construction of complex legal arguments, adhering to the standard persuasive arc: doctrine → theory → normative. It ensures the writing systematically moves from mapping existing law (the problem) to developing a conceptual framework (the why) and finally proposing actionable reforms (the fix). It is essential for building a rigorous, logical, and comprehensive academic argument.

This skill acts as the central orchestrator for contributors to The Yale Law Journal (YLJ). It guides the user through the entire submission lifecycle, from initial topic selection and thesis development to Bluebook citation checking and final placement strategy. It emphasizes that submissions must be near-final, highly structured, and fully footnoted, directing the user to the appropriate skill path based on the article's track (Article, Note, Feature, etc.) and current stage.

A comprehensive guide for building AI agents in Dart with Genkit, covering flows, tools, plugins, schemas, and the Genkit CLI for local development. Ideal for integrating LLMs into Dart or Flutter applications.

This skill acts as a comprehensive map for developing clinical and biomedical NLP pipelines using OpenMed. OpenMed is a local-first Python library that performs named-entity recognition (NER), protected health information (PHI) de-identification, and FHIR export, all running fully on-device without cloud calls. Use this guide when starting a new clinical text project, needing to select the appropriate capability (e.g., zero-shot NER, auditing, or FHIR conversion), or requiring an entry point for the core OpenMed features.

This tool performs a comprehensive analysis of AI configuration file evolution within a Git repository. It tracks when specific AI configs were first introduced, charts monthly commit velocity related to AI features, highlights key PRs that shaped the development, and synthesizes the project's AI maturity phases. Ideal for mapping AI adoption timelines and assessing technical maturity.

A comprehensive tool designed to help developers compare multiple technical approaches for solving a single problem. It systematically identifies and analyzes the trade-offs, complexity, performance, and learning curves associated with different design patterns (e.g., state management, data fetching, API design). Ideal for architectural reviews and making informed, robust technology stack decisions.

This tool analyzes your entire codebase, assessing project identity, team size, test maturity, and existing architectural signals. It then recommends the most appropriate, AI-assisted development methodology stack (e.g., Microservices, Enterprise-Gov) and asks targeted questions to refine the plan, providing a concrete quick start guide.

A comprehensive, multi-step framework for conducting systematic QA testing on web applications. This guide covers scope detection (diff-aware), tiered testing depth (Quick, Standard, Exhaustive), detailed issue taxonomy (Functional, Visual, UX, etc.), structured bug documentation, and the critical fix-and-verify loop, ensuring high code quality throughout the development cycle.

This skill guides a thorough, adversarial review of development plans across four key dimensions: Architecture, Code Quality, Testing, and Performance. It forces explicit consideration of trade-offs, risks, and user approval before any code is written, ensuring robust and well-engineered solutions for complex systems.

This tool analyzes a given codebase to automatically identify high-value, actionable automation routines. It surfaces use cases across three primary trigger types: scheduled cron jobs, external API calls, and GitHub repository events. By mapping manual pain points to autonomous workflows, it helps teams implement robust CI/CD and DevOps practices.

This skill guides users on deploying OpenMed models completely on-device for maximum privacy and edge computing. It details running workflows using three backends: MLX (Apple Silicon), CoreML (iOS/macOS), and ONNX/WebGPU (cross-platform/browser). The process involves converting, optionally quantizing, and running models locally, ensuring PHI never leaves the host device.

This utility is essential for developers needing to ensure that recent code modifications function as intended. By running the application within a verification environment, users can quickly validate that the changes meet all expected behaviors, maintaining system stability and quality assurance before deployment or merging.