Skill UI

This guide provides advanced techniques to optimize AssemblyAI's transcription performance for large-scale applications. Learn how to maximize throughput and minimize latency by implementing caching strategies (Redis/LRU), utilizing webhooks instead of polling, selecting the optimal speech model based on needs, and parallelizing batch processing workloads.

This skill provides comprehensive solutions for managing rate limits and throttling when interacting with the AssemblyAI Speech-to-Text API. It implements advanced patterns like exponential backoff with jitter, queue-based concurrency control, and resilient streaming retry logic. Use it when building high-throughput, reliable applications that process large volumes of audio data.

This advanced guide covers core Anthropic workflow patterns, specifically focusing on tool use (function calling) and building complex, agentic loops. It provides detailed instructions on defining tools using JSON Schema, executing tool calls, and managing the iterative process required for building sophisticated, AI-powered agents using the Anthropic API.

This skill provides comprehensive guides for deploying stateless, serverless API wrappers powered by Claude (Anthropic). It details how to integrate and stream responses from the Messages API across major cloud environments, including Vercel Edge Functions, Fly.io, and Google Cloud Run. It is essential for productionizing AI applications that require robust secret management, scalable infrastructure, and real-time streaming capabilities.

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.

Utilize Anthropic Message Batches API for high-volume, asynchronous bulk processing of AI tasks. This method allows processing up to 10,000 requests per batch, ideal for summarizing large document sets or running repetitive tasks where immediate, synchronous results are not required. The process involves creating the batch, polling for status completion (within 24 hours SLA), and then retrieving detailed results, including success and error statuses for each item.

A specialized skill designed to draft legally binding patent claims based on detailed invention disclosures and prior art reports. It analyzes the core inventive concepts and generates structured claims (independent and dependent) adhering to specific international jurisdictional standards (US, CN, EP) for both methods and apparatuses. This skill defines the legal scope of intellectual property protection.

A persistent memory kit designed for Claude AI agents. It maintains deep context across multiple sessions and projects using a two-layer architecture (hot cache and knowledge wiki). Features include safety hooks, multi-project context isolation, and a /close-day command for daily synthesis, ensuring agents remember complex information and continuously learn.

This comprehensive guide serves as a quick reference for diagnosing and resolving common Cohere API v2 errors and exceptions. It covers specific error codes (400, 429, 401, etc.), details the root causes, and provides actionable code fixes for various tasks, including Chat, Embed, and Rerank. Use this when debugging failed Cohere requests or encountering rate limits.

This comprehensive workflow guides the implementation of a complete Retrieval-Augmented Generation (RAG) pipeline using Cohere's core services. It demonstrates the full process: embedding documents, searching for relevant context, reranking results for precision, and finally generating a grounded answer with precise citations using the Chat API. Ideal for building knowledge-intensive, enterprise-grade Q&A systems.

Provides strategies to optimize costs when utilizing the Cohere API. This guide covers model tier selection (economy, standard, premium), implementing token budgets, reducing embedding storage costs (e.g., using int8), and establishing comprehensive usage trackers. It is essential for maintaining cost predictability in production AI deployments.