Voice AI Development

Voice AI Development

Overview

This public intake copy packages plugins/antigravity-awesome-skills-claude/skills/voice-ai-development from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.

Voice AI Development Expert in building voice AI applications - from real-time voice agents to voice-enabled apps. Covers OpenAI Realtime API, Vapi for voice agents, Deepgram for transcription, ElevenLabs for synthesis, LiveKit for real-time infrastructure, and WebRTC fundamentals. Knows how to build low-latency, production-ready voice experiences. Role: Voice AI Architect You are an expert in building real-time voice applications. You think in terms of latency budgets, audio quality, and user experience. You know that voice apps feel magical when fast and broken when slow. You choose the right combination of providers for each use case and optimize relentlessly for perceived responsiveness. ### Expertise - Real-time audio streaming - Voice agent architecture - Provider selection - Latency optimization - Audio quality tuning

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Capabilities, Prerequisites, Scope, Ecosystem, Patterns, Validation Checks.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• User mentions or implies: voice ai
• User mentions or implies: voice agent
• User mentions or implies: speech to text
• User mentions or implies: text to speech
• User mentions or implies: realtime voice
• User mentions or implies: vapi

Operating Table

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
2. Read the overview and provenance files before loading any copied upstream support files.
3. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
4. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
5. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
6. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
7. Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.

Imported Workflow Notes

Imported: Capabilities

• OpenAI Realtime API
• Vapi voice agents
• Deepgram STT/TTS
• ElevenLabs voice synthesis
• LiveKit real-time infrastructure
• WebRTC audio handling
• Voice agent design
• Latency optimization

Examples

Example 1: Ask for the upstream workflow directly

Use @voice-ai-development to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @voice-ai-development against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @voice-ai-development for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @voice-ai-development using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
• Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
• Treat generated examples as scaffolding; adapt them to the concrete task before execution.
• Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills-claude/skills/voice-ai-development, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

Imported Reference Notes

Imported: Prerequisites

• 0: Async programming
• 1: WebSocket basics
• 2: Audio concepts (sample rate, codec)
• Required skills: Python or Node.js, API keys for providers, Audio handling knowledge

Imported: Scope

• 0: Latency varies by provider
• 1: Cost per minute adds up
• 2: Quality depends on network
• 3: Complex debugging

Imported: Ecosystem

Primary

• OpenAI Realtime API
• Vapi
• Deepgram
• ElevenLabs

Infrastructure

• LiveKit
• Daily.co
• Twilio

Common_integrations

• WebRTC
• WebSockets
• Telephony (SIP/PSTN)

Platforms

• Web applications
• Mobile apps
• Call centers
• Voice assistants

Imported: Patterns

OpenAI Realtime API

Native voice-to-voice with GPT-4o

When to use: When you want integrated voice AI without separate STT/TTS

import asyncio import websockets import json import base64

OPENAI_API_KEY = "sk-..."

async def voice_session(): url = "wss://api.openai.com/v1/realtime?model=gpt-4o-realtime-preview" headers = { "Authorization": f"Bearer {OPENAI_API_KEY}", "OpenAI-Beta": "realtime=v1" }

async with websockets.connect(url, extra_headers=headers) as ws:
    # Configure session
    await ws.send(json.dumps({
        "type": "session.update",
        "session": {
            "modalities": ["text", "audio"],
            "voice": "alloy",  # alloy, echo, fable, onyx, nova, shimmer
            "input_audio_format": "pcm16",
            "output_audio_format": "pcm16",
            "input_audio_transcription": {
                "model": "whisper-1"
            },
            "turn_detection": {
                "type": "server_vad",  # Voice activity detection
                "threshold": 0.5,
                "prefix_padding_ms": 300,
                "silence_duration_ms": 500
            },
            "tools": [
                {
                    "type": "function",
                    "name": "get_weather",
                    "description": "Get weather for a location",
                    "parameters": {
                        "type": "object",
                        "properties": {
                            "location": {"type": "string"}
                        }
                    }
                }
            ]
        }
    }))

    # Send audio (PCM16, 24kHz, mono)
    async def send_audio(audio_bytes):
        await ws.send(json.dumps({
            "type": "input_audio_buffer.append",
            "audio": base64.b64encode(audio_bytes).decode()
        }))

    # Receive events
    async for message in ws:
        event = json.loads(message)

        if event["type"] == "response.audio.delta":
            # Play audio chunk
            audio = base64.b64decode(event["delta"])
            play_audio(audio)

        elif event["type"] == "response.audio_transcript.done":
            print(f"Assistant said: {event['transcript']}")

        elif event["type"] == "input_audio_buffer.speech_started":
            print("User started speaking")

        elif event["type"] == "response.function_call_arguments.done":
            # Handle tool call
            name = event["name"]
            args = json.loads(event["arguments"])
            result = call_function(name, args)
            await ws.send(json.dumps({
                "type": "conversation.item.create",
                "item": {
                    "type": "function_call_output",
                    "call_id": event["call_id"],
                    "output": json.dumps(result)
                }
            }))

Vapi Voice Agent

Build voice agents with Vapi platform

When to use: Phone-based agents, quick deployment

Vapi provides hosted voice agents with webhooks

from flask import Flask, request, jsonify import vapi

app = Flask(name) client = vapi.Vapi(api_key="...")

Create an assistant

assistant = client.assistants.create( name="Support Agent", model={ "provider": "openai", "model": "gpt-4o", "messages": [ { "role": "system", "content": "You are a helpful support agent..." } ] }, voice={ "provider": "11labs", "voiceId": "21m00Tcm4TlvDq8ikWAM" # Rachel }, firstMessage="Hi! How can I help you today?", transcriber={ "provider": "deepgram", "model": "nova-2" } )

Webhook for conversation events

@app.route("/vapi/webhook", methods=["POST"]) def vapi_webhook(): event = request.json

if event["type"] == "function-call":
    # Handle tool call
    name = event["functionCall"]["name"]
    args = event["functionCall"]["parameters"]

    if name == "check_order":
        result = check_order(args["order_id"])
        return jsonify({"result": result})

elif event["type"] == "end-of-call-report":
    # Call ended - save transcript
    transcript = event["transcript"]
    save_transcript(event["call"]["id"], transcript)

return jsonify({"ok": True})

Start outbound call

call = client.calls.create( assistant_id=assistant.id, customer={ "number": "+1234567890" }, phoneNumber={ "twilioPhoneNumber": "+0987654321" } )

Or create web call

web_call = client.calls.create( assistant_id=assistant.id, type="web" )

Returns URL for WebRTC connection

Deepgram STT + ElevenLabs TTS

Best-in-class transcription and synthesis

When to use: High quality voice, custom pipeline

import asyncio from deepgram import DeepgramClient, LiveTranscriptionEvents from elevenlabs import ElevenLabs

Deepgram real-time transcription

deepgram = DeepgramClient(api_key="...")

async def transcribe_stream(audio_stream): connection = deepgram.listen.live.v("1")

async def on_transcript(result):
    transcript = result.channel.alternatives[0].transcript
    if transcript:
        print(f"Heard: {transcript}")
        if result.is_final:
            # Process final transcript
            await handle_user_input(transcript)

connection.on(LiveTranscriptionEvents.Transcript, on_transcript)

await connection.start({
    "model": "nova-2",  # Best quality
    "language": "en",
    "smart_format": True,
    "interim_results": True,  # Get partial results
    "utterance_end_ms": 1000,
    "vad_events": True,  # Voice activity detection
    "encoding": "linear16",
    "sample_rate": 16000
})

# Stream audio
async for chunk in audio_stream:
    await connection.send(chunk)

await connection.finish()

ElevenLabs streaming synthesis

eleven = ElevenLabs(api_key="...")

def text_to_speech_stream(text: str): """Stream TTS audio chunks.""" audio_stream = eleven.text_to_speech.convert_as_stream( voice_id="21m00Tcm4TlvDq8ikWAM", # Rachel model_id="eleven_turbo_v2_5", # Fastest text=text, output_format="pcm_24000" # Raw PCM for low latency )

for chunk in audio_stream:
    yield chunk

Or with WebSocket for lowest latency

async def tts_websocket(text_stream): async with eleven.text_to_speech.stream_async( voice_id="21m00Tcm4TlvDq8ikWAM", model_id="eleven_turbo_v2_5" ) as tts: async for text_chunk in text_stream: audio = await tts.send(text_chunk) yield audio

    # Flush remaining audio
    final_audio = await tts.flush()
    yield final_audio

LiveKit Real-time Infrastructure

WebRTC infrastructure for voice apps

When to use: Building custom real-time voice apps

from livekit import api, rtc import asyncio

Server-side: Create room and tokens

lk_api = api.LiveKitAPI( url="wss://your-livekit.livekit.cloud", api_key="...", api_secret="..." )

async def create_room(room_name: str): room = await lk_api.room.create_room( api.CreateRoomRequest(name=room_name) ) return room

def create_token(room_name: str, participant_name: str): token = api.AccessToken( api_key="...", api_secret="..." ) token.with_identity(participant_name) token.with_grants(api.VideoGrants( room_join=True, room=room_name )) return token.to_jwt()

Agent-side: Connect and process audio

async def voice_agent(room_name: str): room = rtc.Room()

@room.on("track_subscribed")
def on_track(track, publication, participant):
    if track.kind == rtc.TrackKind.KIND_AUDIO:
        # Process incoming audio
        audio_stream = rtc.AudioStream(track)
        asyncio.create_task(process_audio(audio_stream))

token = create_token(room_name, "agent")
await room.connect("wss://your-livekit.livekit.cloud", token)

# Publish agent's audio
source = rtc.AudioSource(sample_rate=24000, num_channels=1)
track = rtc.LocalAudioTrack.create_audio_track("agent-voice", source)
await room.local_participant.publish_track(track)

# Send audio from TTS
async def speak(text: str):
    for audio_chunk in text_to_speech(text):
        await source.capture_frame(rtc.AudioFrame(
            data=audio_chunk,
            sample_rate=24000,
            num_channels=1,
            samples_per_channel=len(audio_chunk) // 2
        ))

return room, speak

Process audio with STT

async def process_audio(audio_stream): async for frame in audio_stream: # Send to Deepgram or other STT await transcriber.send(frame.data)

Full Voice Agent Pipeline

Complete voice agent with all components

When to use: Custom production voice agent

import asyncio from dataclasses import dataclass from typing import AsyncIterator

@dataclass class VoiceAgentConfig: stt_provider: str = "deepgram" tts_provider: str = "elevenlabs" llm_provider: str = "openai" vad_enabled: bool = True interrupt_enabled: bool = True

class VoiceAgent: def init(self, config: VoiceAgentConfig): self.config = config self.is_speaking = False self.conversation_history = []

async def process_audio_stream(
    self,
    audio_in: AsyncIterator[bytes],
    audio_out: asyncio.Queue
):
    """Main audio processing loop."""

    # STT streaming
    async def transcribe():
        transcript_buffer = ""
        async for audio_chunk in audio_in:
            # Check for interruption
            if self.is_speaking and self.config.interrupt_enabled:
                if await self.detect_speech(audio_chunk):
                    await self.stop_speaking()

            result = await self.stt.transcribe(audio_chunk)
            if result.is_final:
                yield result.transcript

    # Process transcripts
    async for user_text in transcribe():
        if not user_text.strip():
            continue

        self.conversation_history.append({
            "role": "user",
            "content": user_text
        })

        # Generate response with streaming
        self.is_speaking = True
        async for audio_chunk in self.generate_response(user_text):
            await audio_out.put(audio_chunk)
        self.is_speaking = False

async def generate_response(self, text: str) -> AsyncIterator[bytes]:
    """Stream LLM response through TTS."""

    # Stream LLM tokens
    llm_stream = self.llm.stream_chat(self.conversation_history)

    # Buffer for TTS (need ~50 chars for good prosody)
    text_buffer = ""
    full_response = ""

    async for token in llm_stream:
        text_buffer += token
        full_response += token

        # Send to TTS when we have enough text
        if len(text_buffer) > 50 or token in ".!?":
            async for audio in self.tts.synthesize_stream(text_buffer):
                yield audio
            text_buffer = ""

    # Flush remaining
    if text_buffer:
        async for audio in self.tts.synthesize_stream(text_buffer):
            yield audio

    self.conversation_history.append({
        "role": "assistant",
        "content": full_response
    })

async def detect_speech(self, audio: bytes) -> bool:
    """Voice activity detection."""
    # Use WebRTC VAD or Silero VAD
    return self.vad.is_speech(audio)

async def stop_speaking(self):
    """Handle interruption."""
    self.is_speaking = False
    # Clear audio queue
    # Stop TTS generation

Latency optimization tips:

1. Use streaming everywhere (STT, LLM, TTS)

2. Start TTS before LLM finishes (~50 char buffer)

3. Use PCM audio format (no encoding overhead)

4. Keep WebSocket connections alive

5. Use regional endpoints close to users

Imported: Validation Checks

Non-Streaming TTS

Severity: HIGH

Message: Non-streaming TTS adds significant latency.

Fix action: Use tts.synthesize_stream() or tts.convert_as_stream()

Hardcoded Sample Rate

Severity: MEDIUM

Message: Hardcoded sample rate may cause format mismatches.

Fix action: Define sample rates as constants, document expected formats

WebSocket Without Reconnection

Severity: HIGH

Message: WebSocket connections need reconnection logic.

Fix action: Add retry loop with exponential backoff

Missing VAD Configuration

Severity: MEDIUM

Message: VAD needs tuning for good user experience.

Fix action: Configure threshold and silence_duration_ms

Blocking Audio Processing

Severity: HIGH

Message: Audio processing should be async to avoid blocking.

Fix action: Use async def and await for audio operations

Missing Interruption Handling

Severity: MEDIUM

Message: Voice agents should handle user interruptions.

Fix action: Add barge-in detection and cancel current response

Audio Queue Without Clear

Severity: LOW

Message: Audio queues should be clearable for interruptions.

Fix action: Add method to clear queue on interruption

WebSocket Without Error Handling

Severity: HIGH

Message: WebSocket operations need error handling.

Fix action: Wrap in try/except for ConnectionClosed

Imported: Collaboration

Delegation Triggers

• agent graph|workflow|state -> langgraph (Need complex agent logic behind voice)
• extract|structured|json -> structured-output (Need to extract structured data from voice)
• observability|tracing|monitoring -> langfuse (Need to monitor voice agent quality)
• frontend|web|react -> nextjs-app-router (Need web interface for voice agent)

Intelligent Voice Agent

Skills: voice-ai-development, langgraph, structured-output

Workflow:

1. Design agent graph with tools
2. Add voice interface layer
3. Use structured output for tool responses
4. Optimize for voice latency

Monitored Voice Agent

Skills: voice-ai-development, langfuse

Workflow:

1. Build voice agent with provider of choice
2. Add Langfuse callbacks
3. Track latency, quality, conversation flow
4. Iterate based on metrics

Phone-based Agent

Skills: voice-ai-development, twilio

Workflow:

1. Set up Vapi or custom agent
2. Connect to Twilio for PSTN
3. Handle inbound/outbound calls
4. Implement call routing logic

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.