Skill UI

A comprehensive guide for optimizing costs when integrating the Speak API. This skill covers usage monitoring, selecting optimal API tiers, and implementing efficient audio processing pipelines. It is essential for developers managing language learning platform operations to ensure cost efficiency while maintaining high performance.

This skill provides a comprehensive guide for securely handling student audio data, assessment records, and learning progress within the Speak platform. It focuses on implementing robust data pipelines while ensuring compliance with international privacy regulations such as GDPR and COPPA. Essential for developers integrating or maintaining language learning applications.

A comprehensive diagnostic bundle designed for developers integrating with the Speak API. It systematically collects critical information, including authentication status, audio format validation (checking codecs, sample rates, and file size using ffprobe), and network connectivity tests for both Speak and OpenAI endpoints. Use this tool when troubleshooting complex language learning integration issues or verifying API setup.

This comprehensive guide demonstrates how to build a robust AI language tutoring session using the Speak SDK. It covers initiating conversations, submitting student responses (text or audio), performing deep pronunciation assessment at the phoneme level, and generating detailed session summaries with grammar and vocabulary metrics. Ideal for developing language education platforms and testing API integration.

A comprehensive guide and setup for building a fast, reliable local development workflow for language learning applications. It includes setting up mock tutor responses for offline testing, generating audio test fixtures using TTS, and implementing a debug mode for detailed pronunciation scoring analysis. Ideal for engineers setting up a new Speak-based integration or refining existing test pipelines.

This guide provides a comprehensive method for optimizing the latency and reliability of the Speak API integration. It covers techniques such as audio preprocessing, implementing response caching, and managing connection pooling. Use this skill when establishing a production-grade, low-latency language learning platform or managing high-volume API operations.

A comprehensive checklist designed for developers to ensure a language learning integration (Speak) is robust and ready for production deployment. It covers critical technical areas including API authentication, audio processing pipelines, rate limiting strategies, monitoring and alerting setup, and adherence to global data compliance standards (e.g., GDPR, FERPA). Use this when implementing or troubleshooting production-grade features.

This guide provides production-ready patterns for integrating the Speak language learning API. It covers managing conversation sessions, performing pronunciation assessment, preprocessing audio files using ffmpeg, implementing robust retry mechanisms (with backoff), and tracking user learning progress. Use this when developing a full-featured, high-reliability language learning application or troubleshooting complex API integrations.

This guide outlines essential security best practices for integrating the Speak API. It covers critical areas like securing API keys using secrets managers, ensuring audio data privacy by deleting local files, and maintaining compliance with major regulations such as COPPA and FERPA. Use this knowledge when building production-ready, secure language learning applications.

This guide provides comprehensive solutions for diagnosing and fixing common technical errors and exceptions encountered when using the TwinMind platform. Whether you are facing API authentication failures, microphone permission issues, transcription timeouts, rate limit exceedances, or audio format incompatibility, this reference offers step-by-step solutions and code examples to ensure smooth operation and successful integration.

This comprehensive workflow automates the entire meeting documentation process. It handles live or recorded audio input, performing accurate transcription with speaker diarization. Subsequently, it leverages advanced AI models to generate detailed summaries, extract key action items, draft follow-up emails, and create structured meeting minutes, significantly enhancing organizational knowledge retention and operational efficiency.

A comprehensive runbook for managing and resolving common incidents within the TwinMind meeting AI platform. This guide covers failures such as transcription not starting, audio capture issues, syncing errors, and calendar disconnections. It provides API setup, code snippets, and detailed troubleshooting steps to ensure a stable, continuous meeting AI workflow.