Skill UI

A comprehensive checklist for deploying Claude API integrations to a production environment. It covers essential best practices for reliability, observability, and cost control, including authentication procedures, advanced error handling, rate limit management, health check implementation, and infrastructure deployment strategies (e.g., canary releases) to ensure a smooth and stable launch.

A comprehensive checklist designed to ensure Claude-powered applications are robust, secure, and production-ready before going live. It guides developers through critical areas, including robust error handling, API security best practices, cost control mechanisms, real-time monitoring setup, and performance optimization, ensuring a stable user experience.

A comprehensive guide to production-ready patterns for integrating with Anthropic's Claude API using both TypeScript and Python SDKs. Covers robust client setup (retries, timeouts), advanced error handling, streaming implementations, prompt caching, and batch processing to ensure high performance and cost efficiency in real-world 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 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.

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 using the Runway SDK. It enables users to perform advanced creative AI tasks, such as generating videos from text prompts or converting images to video, using the official Python SDK. It covers the full workflow, including setup, task execution, output retrieval, and error handling, making it ideal for creative media automation and advanced video production workflows.

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 comprehensive checklist guides the operational setup (MLOps) for AI applications using Together AI's OpenAI-compatible API. It covers critical production concerns, including secure API key management, implementing robust rate limit handling, building error resilience features (circuit breakers, exponential backoff), and setting up comprehensive monitoring for latency, cost control, and model availability. Essential for reliable, scalable LLM deployment.

A comprehensive guide to implementing production-ready patterns for interacting with the Together AI API. This includes a singleton client for seamless OpenAI-compatible integration, robust error wrappers with retry logic, and a fluent request builder. It covers best practices for LLM inference, model setup, and handling diverse response types (chat, embedding, fine-tuning).

This guide provides detailed instructions for optimizing cost and implementing voice-to-text API integrations using Wispr Flow. It covers best practices for WebSocket streaming, REST API usage, context awareness (like programming code), and robust error handling, helping developers build advanced voice-powered applications.