Skill UI

This SDK provides programmatic access to Azure AI Content Understanding, enabling the extraction of structured and semantic content from diverse multimodal sources. It supports analyzing documents (PDF, etc.), images, audio, and video files. Users can leverage prebuilt analyzers (like invoice or document search) or create custom analyzers with specific field schemas for advanced RAG and automated workflow implementations.

This SDK provides programmatic access to Azure AI Content Understanding, enabling the extraction of structured and semantic content from diverse multimodal sources. It supports analyzing documents (PDF, etc.), images, audio, and video files. Users can leverage prebuilt analyzers (like invoice or document search) or create custom analyzers with specific field schemas for advanced RAG and automated workflow implementations.

Automates Figma workflows through Rube MCP and Composio, covering file inspection, component/token extraction, exports, comments, and team browsing while guiding schema discovery before each run.

This advanced workflow executes sophisticated web data gathering tasks. It supports three primary functions: LLM-powered structured data extraction using custom JSON schemas or prompts; efficient batch scraping of multiple known URLs; and rapid site mapping to discover all reachable URLs on a domain. Use this when you require reliable, typed data structures rather than raw markdown content.

This skill translates complex business requirements and specification documents into a complete, production-ready Postgres schema for Supabase. It covers the full development lifecycle, including entity extraction, defining robust tables with constraints, generating migration SQL files, implementing Row Level Security (RLS) policies, and producing corresponding TypeScript types. It is the foundational step for building any robust SaaS application on Supabase.

Use Composio's Zenrows toolkit through a Rube MCP integration to automate data extraction tasks, always probing `RUBE_SEARCH_TOOLS` for current schemas, reconciling connections via `RUBE_MANAGE_CONNECTIONS`, and invoking discovered operations with `RUBE_MULTI_EXECUTE_TOOL` while reusing sessions.

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.