claude

conversational-ai-voice-engineering

Conversational voice-AI engineering team — agents for production real-time voice agents: voice-ai-architect (cascade STT->LLM->TTS vs speech-to-speech, the latency budget, turn-taking / barge-in / endpointing, channel choice, build-vs-platform across Twilio / Vapi / Retell / LiveKit / Pipecat, guardrails), speech-pipeline-engineer (ASR & TTS provider choice, streaming transcription, VAD/endpointing, diarization, prosody/SSML, codecs, noise, WER), and dialog-and-integration-engineer (state, LLM orchestration and mid-call tool calling, SIP/PSTN telephony, IVR/DTMF, call routing, flow testing and eval). Ships skills, a knowledge bank (Mermaid decision trees + a dated 2026 reference), best-practices, templates, commands. Engineering judgment, not legal/compliance advice; the ASR/TTS/platform/telephony landscape is volatile — versions and latency numbers carry a retrieval date + [verify-at-use]; no PII, and call audio/transcripts are sensitive. Requires ravenclaude-core@>=0.7.0.

9 on GitHubby Matt Corbettv0.1.1MITupdated today
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Plugin conversational-ai-voice-engineering from marketplace ravenclaude in mcorbett51090/RavenClaude. Commands read from the repo's manifest — today.

What it installs

Skills (4)

  • dialog-management-and-tool-calling

    Design the dialog layer of a voice agent: explicit dialog-state modeling, LLM orchestration, context/window management over a long call, and mid-call function/tool calling that hides latency with natural fillers and treats tool failure as a first-class branch. Design the unhappy path (silence, misre

  • speech-recognition-and-synthesis

    Engineer the ASR and TTS layer of a voice agent: choose STT/TTS providers on the channel (narrowband telephony vs wideband web), languages, streaming support, WER, and cost; stream transcription and synthesis to cut latency; tune VAD/endpointing; handle diarization, prosody/SSML, codecs & sample rat

  • telephony-and-call-flow-integration

    Integrate a voice agent with real telephony and validate it: SIP/PSTN trunking and media handling, WebRTC for web/app, DTMF capture (in-band vs RFC 2833/SIP INFO), IVR-vs-conversational per step, call routing and warm/cold human transfer with context, and a voice-agent eval harness scoring task succ

  • voice-agent-architecture-and-latency

    Choose the voice-agent pipeline shape (cascade STT->LLM->TTS vs speech-to-speech), the channel (telephony / web / SDK), and the build-vs-platform bet (Twilio / Vapi / Retell / LiveKit / Pipecat), then allocate the end-to-end latency budget per hop and design the turn-taking / barge-in / fallback mod

Slash commands (2)

  • /conversational-ai-voice-engineering:design-voice-agent

    Design a production voice agent: choose cascade vs speech-to-speech, the channel (telephony / web / SDK), and the build-vs-platform bet (Twilio / Vapi / Retell / LiveKit / Pipecat), then allocate the end-to-end latency budget and design turn-taking / barge-in / fallback (model/platform specifics ver

  • /conversational-ai-voice-engineering:plan-voice-latency-budget

    Allocate the end-to-end voice latency budget per hop (VAD/endpointing, STT, LLM TTFT, TTS TTFB, network), triage the dominant hop from an end-to-end measurement, and produce an ordered latency-reduction plan that holds sub-second conversational feel (latency numbers verify-at-use).

Subagents (3)

  • dialog-and-integration-engineer

    Voice dialog + integration: dialog/state management, LLM orchestration + mid-call tool calling, SIP/telephony integration, IVR/DTMF, call routing/transfer, conversation-flow testing & eval (task success, WER, latency). NOT ASR/TTS model tuning -> speech-pipeline-engineer.

  • speech-pipeline-engineer

    ASR & TTS engineering: STT/TTS provider choice (Whisper, Deepgram, ElevenLabs), streaming transcription, VAD & endpointing tuning, diarization, prosody/SSML, codecs & sample rates, noise robustness, wake-word, word error rate. NOT system architecture/latency budget -> voice-ai-architect.

  • voice-ai-architect

    Voice-AI system architecture: cascade (STT->LLM->TTS) vs speech-to-speech, end-to-end latency budget, turn-taking/barge-in/endpointing, channel choice, build-vs-platform (Twilio/Vapi/Retell/LiveKit/Pipecat), guardrails/fallback. NOT ASR/TTS model tuning -> speech-pipeline-engineer.

View source on GitHub mcorbett51090.github.io

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