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Build real-time conversational AI voice engines using async worker pipelines, streaming transcription, LLM agents, and TTS synthesis with interrupt handling and multi-provider support
Passed 17 of 22 test cases with the skill, and 9 of 22 without it.
and 5 more in the full report
8 fixed9 passed either way5 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 8 test cases the skill fixed. Same model, same prompt, with and without the skill.
We are building a WebRTC Selective Forwarding Unit (SFU) in Node.js using mediasoup to handle multi-party video conferencing rooms with screen sharing. How should we configure the Transport and Router objects to manage media track forwarding across participants?
| Checked | Without skill | With skill |
|---|---|---|
| Explains mediasoup WebRTC router creation and transport connect events for audio video tracks. | Missed | Passed |
| Does not model multi-party conferencing using voice AI agent worker queues. | Passed | Passed |
The instructions your model reads when the skill loads.
This skill guides you through building production-ready voice AI engines with real-time conversation capabilities. Voice AI engines enable natural, bidirectional conversations between users and AI agents through streaming audio processing, speech-to-text transcription, LLM-powered responses, and text-to-speech synthesis.
The core architecture uses an async queue-based worker pipeline where each component runs independently and communicates via asyncio.Queue objects, enabling concurrent processing, interrupt handling, and real-time streaming at every stage.
Use this skill when:
Every voice AI engine follows this pipeline:
Audio In → Transcriber → Agent → Synthesizer → Audio Out
(Worker 1) (Worker 2) (Worker 3)
Key Benefits:
Every worker follows this pattern:
test cases passed, vs 9 / 22 without
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