Skill UI

This module provides comprehensive observability for Anthropic's Claude API integrations. It enables the tracking of critical operational metrics including latency, token usage (input/output), estimated API costs, and error rates. Use it to set up structured logging and Prometheus metrics for robust monitoring and alerting, ensuring reliable and cost-effective AI application deployments.

This comprehensive skill provides multi-layered guardrails for integrating with LLMs like Claude. It enforces security and compliance by implementing input validation (PII detection, length checks), system prompt restriction (scope control, safety rules), output filtering (preventing sensitive data leakage), cost governance, and model access policies. Ideal for building secure, robust, and governed AI applications.

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.

Implements robust rate limiting, backoff logic, and quota management for the Anthropic Claude API. It handles 429 errors and optimizes request throughput by managing Requests Per Minute (RPM), Input Tokens Per Minute (ITPM), and Output Tokens Per Minute (OTPM). This skill provides advanced strategies including SDK retries, custom header awareness, queue-based throttling, and batch processing for stable, high-throughput production applications.

A comprehensive guide to using AssemblyAI for rich audio processing. This guide covers basic speech-to-text transcription from URLs or local files, along with advanced AI features. Capabilities include speaker diarization, sentiment analysis, key phrase extraction, entity detection, and powerful LLM-powered analysis (LeMUR) for summarization and Q&A. Ideal for integrating deep, multi-faceted audio insights into applications.

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.