Skill UI

Provides structured methods and code examples to optimize Abridge's clinical AI integration for high-volume, enterprise deployments. Focuses on reducing critical latencies across the entire workflow, including audio streaming throughput, note generation pipelines, and secure FHIR push operations, ensuring scalability for large health systems.

Provides a comprehensive architectural blueprint for integrating ambient AI solutions (Abridge) into multi-site health systems. It guides the full process from ambient audio capture to structured note generation, covering data flow, component design, FHIR R4 standards, HIPAA compliance, and integration patterns with major EHRs like Epic and Athena.

This skill provides a comprehensive workflow for generating professional and structured financial reports. It guides the user through planning, writing, and editing various sections, and can also generate necessary chart configurations using visual tools. Ideal for condensing complex financial analysis into clear, actionable reports.

This advanced workflow allows developers to clone live websites directly into structured React or HTML code using the Anima platform. It goes beyond simple cloning by featuring post-generation customization rules (e.g., updating utility classes or imports) and a dedicated design token mapper. This ensures the generated code is immediately usable and adheres strictly to the project's existing design system tokens, while also organizing the final component structure.

This expert acts as a dedicated data analysis partner for Product Managers. It helps users explore data, write complex queries (e.g., SQL, BigQuery), analyze key metrics, and create structured dashboards. Use this tool when you need to derive actionable insights from complex datasets, compare user cohorts, or understand product performance trends to inform critical business decisions.

This skill automatically converts structured data specifications (such as CSV, JSON schemas, SQL DDL, or Protobuf definitions) into functional, production-ready code. It generates complete models, validation logic, full CRUD API endpoints, and unit tests in multiple languages (Python, TypeScript, Go, etc.), significantly reducing boilerplate coding time and ensuring type safety.

This comprehensive workflow provides core CRUD (Create, Read, Update, Delete) operations for Notion integrations. It allows users to perform complex database queries using nested filters (AND/OR) across various property types (select, date, number, etc.). Furthermore, it facilitates the structured creation and updating of pages, making it essential for automating data management, content generation, and complex information retrieval within Notion.

A comprehensive guide and implementation pattern for handling common errors when working with the Runway AI platform. This skill provides Python SDK examples for tasks like image-to-video generation, along with structured solutions for API issues such as 'Unauthorized' (401) or 'Insufficient credits' (402), ensuring smooth and reliable creative AI workflows.

This debug bundle provides development patterns and SDK integration for RunwayML, a powerful AI video generation and creative platform. Users can leverage this bundle to perform tasks like image-to-video conversion and advanced content creation. It includes structured examples and robust error handling, making it ideal for developers needing to integrate professional-grade AI video capabilities into their applications.

Generates structured DESIGN.md files that serve as a single source of truth for AI-powered screen generation (Google Stitch). This skill translates complex, anti-generic design directives into precise semantic rules, covering typography, color calibration, motion physics, and layout principles to enforce a premium, high-agency UI/UX standard. It ensures outputs are sophisticated and avoid common AI design clichés.

This skill synthesizes a comprehensive Product Requirements Document (PRD) from the current conversation context and understanding of the codebase. It guides the user through structuring the problem statement, defining detailed user stories, outlining implementation decisions, and suggesting test strategies. The final PRD is formatted and submitted directly as a GitHub issue, ensuring a structured handover from discussion to development specification.

A comprehensive workflow guide for fine-tuning open-source Large Language Models (LLMs) using the Together AI API. This process covers preparing structured training data (JSONL format), uploading the file, initiating the fine-tuning job, monitoring the training status, and finally deploying the custom model for inference. Ideal for developers needing to customize models for specific enterprise tasks, such as customer support or specialized content generation.