Skill UI

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.

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.

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.

This comprehensive guide details the process of installing and configuring the Cohere SDK (v2) in both Node.js and Python environments. It covers initial setup, setting API keys via environment variables, and provides full code examples for verifying connectivity across various Cohere endpoints, including Chat, Embed, and Rerank, enabling immediate integration into your AI projects.

A comprehensive guide for optimizing Cohere API v2 performance. Learn advanced strategies to improve throughput, reduce latency, and manage costs when using Chat, Embed, and Rerank endpoints. Techniques covered include dynamic model selection based on latency budgets, implementing streaming for faster perceived response times, efficient batch processing for embeddings, vector compression, and robust caching mechanisms.

This guide details the process of deploying production-grade inference services on CoreWeave using KServe. It covers setting up autoscaling, GPU scheduling, and implementing scale-to-zero functionality for efficient ML model serving endpoints. Ideal for MLOps engineers deploying large language models (LLMs) or other complex AI models in a production environment.

A comprehensive workflow suite for professional audio manipulation using ElevenLabs API. This module supports multiple advanced AI features, including Speech-to-Speech voice cloning, generating realistic sound effects from text prompts, isolating and removing background noise, and accurate Speech-to-Text transcription with diarization. Ideal for content creators, podcast producers, and audio engineers.

This skill provides methods to optimize ElevenLabs usage costs for Text-to-Speech (TTS) workloads. It covers model selection (e.g., using Flash/Turbo for savings), implementing character-efficient text pre-processing to reduce character count, and setting up real-time quota monitoring with budget alerts. Ideal for developers managing high-volume, cost-sensitive voice applications.

This guide provides a robust implementation for handling asynchronous webhooks from ElevenLabs. It covers the critical steps of receiving POST notifications (such as transcription completion, post-call audio, and failure events), performing secure HMAC-SHA256 signature verification, and routing the event data to specific processing handlers. Ideal for integrating ElevenLabs into conversational AI pipelines, ensuring data integrity and reliable background processing.

This bridge provides bidirectional integration between Home Assistant (HA) and advanced AI pipelines. It streams HA camera feeds into the AI system for sophisticated analysis (detection, VLM, REID). Conversely, the analysis results—such as detected objects, descriptions, and person identities—are sent back to HA as image_processing entities, enabling powerful automation and smart notifications.