Skill UI

This workflow demonstrates the core capabilities of the Glean Client API, allowing users to build comprehensive knowledge assistants. It covers advanced full-text search with filters and facets, implements AI-powered chat for contextual Q&A, and provides autocomplete suggestions. Ideal for integrating deep search and conversational AI into enterprise applications.

This guide addresses critical LangGraph streaming pitfalls, including state overdraw, proxy buffering issues (Nginx, Cloud Run), and excessive event noise. It provides a production-grade pattern for implementing robust SSE/WebSocket endpoints using FastAPI. Learn how to correctly select the `stream_mode` (messages, updates, values) and implement server-side event filtering to reliably build live-token chat UIs and perform comprehensive state visualization.

A remote MCP server designed for AI agents to dynamically discover, inspect, and verify external tools, APIs, and services. It allows agents to search a curated index of AI-ready websites, check for machine-readable endpoints (like OpenAPI or MCP), and confirm if an endpoint responds correctly to JSON-RPC, enabling robust, dynamic agent workflow building.

Qdrant is a high-performance vector database. This client SDK suite provides official, easy-to-use libraries for multiple programming languages (including Python, Java, Go, and TypeScript). It enables developers to seamlessly integrate sophisticated vector search capabilities into their applications, supporting both REST and gRPC APIs. Ideal for building semantic search engines, recommendation systems, and advanced AI applications.

This skill integrates with the Runway ML platform, enabling developers to programmatically generate high-quality video and edit images using the official Python SDK. Use this when your workflow requires advanced creative AI features, such as converting text prompts into videos, or managing complex creative tasks within a CI/CD pipeline. The skill provides necessary patterns and error handling for successful API calls.

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 skill provides implementation patterns and an SDK pattern for utilizing the RunwayML API. It allows users to perform advanced video generation, image-to-video tasks, and creative AI workflows directly within their code. Use cases include generating cinematic content, editing media, and managing API credits for complex creative projects.

This guide provides detailed, step-by-step instructions for installing and authenticating the Runway ML SDK. It covers prerequisites for both Python and Node.js environments, including setting up API keys and verifying the connection. This setup is essential for developers who plan to perform high-fidelity AI video generation, image editing, or advanced creative content workflows using the Runway platform.

This skill provides the implementation pattern and comprehensive guide for integrating the Runway ML API. It demonstrates using the Python SDK to perform advanced creative tasks, such as image-to-video generation. Furthermore, it includes crucial error handling instructions for managing common issues like API rate limits, unauthorized access (401), and credit exhaustion.

This skill provides implementation patterns for utilizing Runway's AI video generation and creative platform. It covers setting up the SDK, generating videos from images, and handling common API errors (like insufficient credits or unauthorized access). It is designed for developers needing to integrate secure, high-quality AI video workflows into their applications.

This skill provides comprehensive integration patterns for the RunwayML platform, specializing in advanced AI video and image generation. Developers can programmatically create high-quality video content from text prompts or images, supporting sophisticated creative workflows. It is essential for building applications that require programmatic access to industry-leading creative AI capabilities.

This workflow guides users through the complete lifecycle of customizing large language models (LLMs). It covers uploading training data, initiating fine-tuning jobs, monitoring training progress, managing model versions, and finally deploying the optimized model to a dedicated, autoscaling inference endpoint using the Together AI API. Ideal for production-level customization.