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Voice agents represent the frontier of AI interaction - humans speaking naturally with AI systems.
Passed 20 of 22 test cases with the skill, and 16 of 22 without it.
and 2 more in the full report
4 fixed16 passed either way2 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 4 test cases the skill fixed. Same model, same prompt, with and without the skill.
We are assembling a custom pipeline voice agent (STT -> LLM -> TTS) and need component choices that keep total latency under 800ms. We are currently considering OpenAI Whisper batch API for STT, GPT-4o for LLM, and standard ElevenLabs v1 for TTS. Recommend an optimized stack of models and streaming settings.
| Checked | Without skill | With skill |
|---|---|---|
| Recommends streaming STT with Deepgram Nova-3 instead of batch Whisper. | Missed | Passed |
| Recommends ElevenLabs Flash or Deepgram Aura-2 for low latency text-to-speech. | Passed | Passed |
| Recommends streaming LLM output directly to text-to-speech. | Passed | Passed |
The instructions your model reads when the skill loads.
Voice agents represent the frontier of AI interaction - humans speaking naturally with AI systems. The challenge isn't just speech recognition and synthesis, it's achieving natural conversation flow with sub-800ms latency while handling interruptions, background noise, and emotional nuance.
This skill covers two architectures: speech-to-speech (OpenAI Realtime API, lowest latency, most natural) and pipeline (STT→LLM→TTS, more control, easier to debug). Key insight: latency is the constraint. Humans expect responses in 500ms. Every millisecond matters.
84% of organizations are increasing voice AI budgets in 2025. This is the year voice agents go mainstream.
test cases passed, vs 16 / 22 without
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