Skill UI

A comprehensive setup guide for establishing a fast, reproducible local development environment for AssemblyAI projects. It incorporates hot reloading, local caching of transcription results, and mocked testing utilities to ensure rapid iteration cycles without relying on live API keys or incurring costs. Ideal for setting up new services or enhancing existing AI workflows.

This workflow provides comprehensive audio processing capabilities, covering both real-time streaming transcription (for live captions and voice agents) and advanced post-processing using LeMUR. Use it for summarizing long meetings, answering questions based on transcripts, extracting action items, and running custom LLM analyses on recorded audio.

This skill provides a comprehensive guide to optimizing costs when using AssemblyAI for speech-to-text transcription. It details model selection (Best vs. Nano), guides feature budgeting (e.g., Diarization, Sentiment Analysis), and includes code examples for cost estimation and usage tracking. Use this when managing billing, reducing operational expenses, or implementing budget alerts for high-volume audio processing.

This guide provides comprehensive instructions for deploying AssemblyAI-powered transcription services to major cloud platforms like Vercel, Google Cloud Run, and Fly.io. It covers essential steps including secure secret management, setting up webhook endpoints for asynchronous notifications, and implementing streaming token endpoints for real-time client use in production environments.

A comprehensive, production-ready reference architecture for building robust, scalable transcription services using AssemblyAI. This guide demonstrates best practices for layered system design, covering API endpoints, service logic, client management, and infrastructure integration (webhooks, database). It is ideal for designing new high-throughput speech-to-text applications.

Implement robust handling for AssemblyAI's asynchronous transcription completion events via webhooks. This guides users on how to set up secure endpoints that receive POST requests upon job completion or failure. It covers submitting transcripts with webhook URLs, verifying authentication headers, and processing the final transcript data, including running subsequent analyses like summarization.

This guide demonstrates how to set up robust Continuous Integration/Continuous Deployment (CI/CD) pipelines for projects utilizing AssemblyAI's speech-to-text services. It provides detailed GitHub Actions workflows for running both mocked unit tests (free and fast) and live, cost-controlled integration tests. Learn how to safely manage API keys and ensure transcription stability in your automated build process.

A comprehensive guide designed to diagnose and resolve common errors and exceptions encountered when using the AssemblyAI API. It covers critical topics such as authentication failures (401), rate limiting (429), transcription status errors, streaming issues, and LeMUR processing, helping users debug and maintain robust data pipelines.

Execute comprehensive, asynchronous transcription workflows using AssemblyAI. This tool handles file upload, queues jobs, and provides advanced audio intelligence features. Capabilities include speaker diarization, sentiment analysis, named entity recognition (NER), topic categorization (IAB), content safety moderation, and sensitive PII redaction. Ideal for processing meeting recordings, interviews, and large volumes of spoken content.

Collects comprehensive diagnostic evidence for support tickets related to AssemblyAI. This utility automates the gathering of runtime environment details, SDK versions, API connectivity checks, and specific transcript statuses, greatly accelerating troubleshooting and issue resolution for speech-to-text 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.