[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6F0B3BNE9X8YEYF2JV0XPRK":3,"skill-same:01M6F0B3BNE9X8YEYF2JV0XPRK":84},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":28,"publishedAt":49,"linkOnly":50,"markdownContent":51,"markdownBody":52,"frontMatter":53,"refName":55,"commitSha":56,"skillDirectory":57,"forksCount":58,"repositoryHtmlUrl":59,"repositoryHomepageUrl":60,"ownerHtmlUrl":61,"ownerBlogUrl":6,"ownerDescription":62,"updatedAt":63,"install":64,"resources":77},"01M6F0B3BNE9X8YEYF2JV0XPRK","voice-ai-development",null,"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. Use when: voice ai, voice agent, speech to text, text to speech, realtime voice.","ComeOnOliver\u002Fskillshub","ComeOnOliver","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F43215937?v=4","01M6E5JNFECJAAVFE0W7SMWWD2","Haoran Shu","01M6E5JNNDQS7AJZDYFQ8TPZCK",67,"MIT","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Faiskillstore\u002Fmarketplace\u002Fsickn33\u002Fvoice-ai-development\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48],"development","voice ai development","voice","openai realtime","infrastructure","how to","apps","voice agent","time","webrtc","elevenlabs","expert","ready","text","how","api","voice agents","apache","speech","openai","2026-11-10 10:12:30",false,"---\nname: voice-ai-development\ndescription: \"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. Use when: voice ai, voice agent, speech to text, text to speech, realtime voice.\"\nsource: vibeship-spawner-skills (Apache 2.0)\n---\n\n# Voice AI Development\n\n**Role**: Voice AI Architect\n\nYou are an expert in building real-time voice applications. You think in terms of\nlatency budgets, audio quality, and user experience. You know that voice apps feel\nmagical when fast and broken when slow. You choose the right combination of providers\nfor each use case and optimize relentlessly for perceived responsiveness.\n\n## Capabilities\n\n- OpenAI Realtime API\n- Vapi voice agents\n- Deepgram STT\u002FTTS\n- ElevenLabs voice synthesis\n- LiveKit real-time infrastructure\n- WebRTC audio handling\n- Voice agent design\n- Latency optimization\n\n## Requirements\n\n- Python or Node.js\n- API keys for providers\n- Audio handling knowledge\n\n## Patterns\n\n### OpenAI Realtime API\n\nNative voice-to-voice with GPT-4o\n\n**When to use**: When you want integrated voice AI without separate STT\u002FTTS\n\n```python\nimport asyncio\nimport websockets\nimport json\nimport base64\n\nOPENAI_API_KEY = \"sk-...\"\n\nasync def voice_session():\n    url = \"wss:\u002F\u002Fapi.openai.com\u002Fv1\u002Frealtime?model=gpt-4o-realtime-preview\"\n    headers = {\n        \"Authorization\": f\"Bearer {OPENAI_API_KEY}\",\n        \"OpenAI-Beta\": \"realtime=v1\"\n    }\n\n    async with websockets.connect(url, extra_headers=headers) as ws:\n        # Configure session\n        await ws.send(json.dumps({\n            \"type\": \"session.update\",\n            \"session\": {\n                \"modalities\": [\"text\", \"audio\"],\n                \"voice\": \"alloy\",  # alloy, echo, fable, onyx, nova, shimmer\n                \"input_audio_format\": \"pcm16\",\n                \"output_audio_format\": \"pcm16\",\n                \"input_audio_transcription\": {\n                    \"model\": \"whisper-1\"\n                },\n                \"turn_detection\": {\n                    \"type\": \"server_vad\",  # Voice activity detection\n                    \"threshold\": 0.5,\n                    \"prefix_padding_ms\": 300,\n                    \"silence_duration_ms\": 500\n                },\n                \"tools\": [\n                    {\n                        \"type\": \"function\",\n                        \"name\": \"get_weather\",\n                        \"description\": \"Get weather for a location\",\n                        \"parameters\": {\n                            \"type\": \"object\",\n                            \"properties\": {\n                                \"location\": {\"type\": \"string\"}\n                            }\n                        }\n                    }\n                ]\n            }\n        }))\n\n        # Send audio (PCM16, 24kHz, mono)\n        async def send_audio(audio_bytes):\n            await ws.send(json.dumps({\n                \"type\": \"input_audio_buffer.append\",\n                \"audio\": base64.b64encode(audio_bytes).decode()\n            }))\n\n        # Receive events\n        async for message in ws:\n            event = json.loads(message)\n\n            if event[\"type\"] == \"resp\n```\n\n### Vapi Voice Agent\n\nBuild voice agents with Vapi platform\n\n**When to use**: Phone-based agents, quick deployment\n\n```python\n# Vapi provides hosted voice agents with webhooks\n\nfrom flask import Flask, request, jsonify\nimport vapi\n\napp = Flask(__name__)\nclient = vapi.Vapi(api_key=\"...\")\n\n# Create an assistant\nassistant = client.assistants.create(\n    name=\"Support Agent\",\n    model={\n        \"provider\": \"openai\",\n        \"model\": \"gpt-4o\",\n        \"messages\": [\n            {\n                \"role\": \"system\",\n                \"content\": \"You are a helpful support agent...\"\n            }\n        ]\n    },\n    voice={\n        \"provider\": \"11labs\",\n        \"voiceId\": \"21m00Tcm4TlvDq8ikWAM\"  # Rachel\n    },\n    firstMessage=\"Hi! How can I help you today?\",\n    transcriber={\n        \"provider\": \"deepgram\",\n        \"model\": \"nova-2\"\n    }\n)\n\n# Webhook for conversation events\n@app.route(\"\u002Fvapi\u002Fwebhook\", methods=[\"POST\"])\ndef vapi_webhook():\n    event = request.json\n\n    if event[\"type\"] == \"function-call\":\n        # Handle tool call\n        name = event[\"functionCall\"][\"name\"]\n        args = event[\"functionCall\"][\"parameters\"]\n\n        if name == \"check_order\":\n            result = check_order(args[\"order_id\"])\n            return jsonify({\"result\": result})\n\n    elif event[\"type\"] == \"end-of-call-report\":\n        # Call ended - save transcript\n        transcript = event[\"transcript\"]\n        save_transcript(event[\"call\"][\"id\"], transcript)\n\n    return jsonify({\"ok\": True})\n\n# Start outbound call\ncall = client.calls.create(\n    assistant_id=assistant.id,\n    customer={\n        \"number\": \"+1234567890\"\n    },\n    phoneNumber={\n        \"twilioPhoneNumber\": \"+0987654321\"\n    }\n)\n\n# Or create web call\nweb_call = client.calls.create(\n    assistant_id=assistant.id,\n    type=\"web\"\n)\n# Returns URL for WebRTC connection\n```\n\n### Deepgram STT + ElevenLabs TTS\n\nBest-in-class transcription and synthesis\n\n**When to use**: High quality voice, custom pipeline\n\n```python\nimport asyncio\nfrom deepgram import DeepgramClient, LiveTranscriptionEvents\nfrom elevenlabs import ElevenLabs\n\n# Deepgram real-time transcription\ndeepgram = DeepgramClient(api_key=\"...\")\n\nasync def transcribe_stream(audio_stream):\n    connection = deepgram.listen.live.v(\"1\")\n\n    async def on_transcript(result):\n        transcript = result.channel.alternatives[0].transcript\n        if transcript:\n            print(f\"Heard: {transcript}\")\n            if result.is_final:\n                # Process final transcript\n                await handle_user_input(transcript)\n\n    connection.on(LiveTranscriptionEvents.Transcript, on_transcript)\n\n    await connection.start({\n        \"model\": \"nova-2\",  # Best quality\n        \"language\": \"en\",\n        \"smart_format\": True,\n        \"interim_results\": True,  # Get partial results\n        \"utterance_end_ms\": 1000,\n        \"vad_events\": True,  # Voice activity detection\n        \"encoding\": \"linear16\",\n        \"sample_rate\": 16000\n    })\n\n    # Stream audio\n    async for chunk in audio_stream:\n        await connection.send(chunk)\n\n    await connection.finish()\n\n# ElevenLabs streaming synthesis\neleven = ElevenLabs(api_key=\"...\")\n\ndef text_to_speech_stream(text: str):\n    \"\"\"Stream TTS audio chunks.\"\"\"\n    audio_stream = eleven.text_to_speech.convert_as_stream(\n        voice_id=\"21m00Tcm4TlvDq8ikWAM\",  # Rachel\n        model_id=\"eleven_turbo_v2_5\",  # Fastest\n        text=text,\n        output_format=\"pcm_24000\"  # Raw PCM for low latency\n    )\n\n    for chunk in audio_stream:\n        yield chunk\n\n# Or with WebSocket for lowest latency\nasync def tts_websocket(text_stream):\n    async with eleven.text_to_speech.stream_async(\n        voice_id=\"21m00Tcm4TlvDq8ikWAM\",\n        model_id=\"eleven_turbo_v2_5\"\n    ) as tts:\n        async for text_chunk in text_stream:\n            audio = await tts.send(text_chunk)\n            yield audio\n\n        # Flush remaining audio\n        final_audio = await tts.flush()\n        yield final_audio\n```\n\n## Anti-Patterns\n\n### ❌ Non-streaming Pipeline\n\n**Why bad**: Adds seconds of latency.\nUser perceives as slow.\nLoses conversation flow.\n\n**Instead**: Stream everything:\n- STT: interim results\n- LLM: token streaming\n- TTS: chunk streaming\nStart TTS before LLM finishes.\n\n### ❌ Ignoring Interruptions\n\n**Why bad**: Frustrating user experience.\nFeels like talking to a machine.\nWastes time.\n\n**Instead**: Implement barge-in detection.\nUse VAD to detect user speech.\nStop TTS immediately.\nClear audio queue.\n\n### ❌ Single Provider Lock-in\n\n**Why bad**: May not be best quality.\nSingle point of failure.\nHarder to optimize.\n\n**Instead**: Mix best providers:\n- Deepgram for STT (speed + accuracy)\n- ElevenLabs for TTS (voice quality)\n- OpenAI\u002FAnthropic for LLM\n\n## Limitations\n\n- Latency varies by provider\n- Cost per minute adds up\n- Quality depends on network\n- Complex debugging\n\n## Related Skills\n\nWorks well with: `langgraph`, `structured-output`, `langfuse`\n","# Voice AI Development\n\n**Role**: Voice AI Architect\n\nYou are an expert in building real-time voice applications. You think in terms of\nlatency budgets, audio quality, and user experience. You know that voice apps feel\nmagical when fast and broken when slow. You choose the right combination of providers\nfor each use case and optimize relentlessly for perceived responsiveness.\n\n## Capabilities\n\n- OpenAI Realtime API\n- Vapi voice agents\n- Deepgram STT\u002FTTS\n- ElevenLabs voice synthesis\n- LiveKit real-time infrastructure\n- WebRTC audio handling\n- Voice agent design\n- Latency optimization\n\n## Requirements\n\n- Python or Node.js\n- API keys for providers\n- Audio handling knowledge\n\n## Patterns\n\n### OpenAI Realtime API\n\nNative voice-to-voice with GPT-4o\n\n**When to use**: When you want integrated voice AI without separate STT\u002FTTS\n\n```python\nimport asyncio\nimport websockets\nimport json\nimport base64\n\nOPENAI_API_KEY = \"sk-...\"\n\nasync def voice_session():\n    url = \"wss:\u002F\u002Fapi.openai.com\u002Fv1\u002Frealtime?model=gpt-4o-realtime-preview\"\n    headers = {\n        \"Authorization\": f\"Bearer {OPENAI_API_KEY}\",\n        \"OpenAI-Beta\": \"realtime=v1\"\n    }\n\n    async with websockets.connect(url, extra_headers=headers) as ws:\n        # Configure session\n        await ws.send(json.dumps({\n            \"type\": \"session.update\",\n            \"session\": {\n                \"modalities\": [\"text\", \"audio\"],\n                \"voice\": \"alloy\",  # alloy, echo, fable, onyx, nova, shimmer\n                \"input_audio_format\": \"pcm16\",\n                \"output_audio_format\": \"pcm16\",\n                \"input_audio_transcription\": {\n                    \"model\": \"whisper-1\"\n                },\n                \"turn_detection\": {\n                    \"type\": \"server_vad\",  # Voice activity detection\n                    \"threshold\": 0.5,\n                    \"prefix_padding_ms\": 300,\n                    \"silence_duration_ms\": 500\n                },\n                \"tools\": [\n                    {\n                        \"type\": \"function\",\n                        \"name\": \"get_weather\",\n                        \"description\": \"Get weather for a location\",\n                        \"parameters\": {\n                            \"type\": \"object\",\n                            \"properties\": {\n                                \"location\": {\"type\": \"string\"}\n                            }\n                        }\n                    }\n                ]\n            }\n        }))\n\n        # Send audio (PCM16, 24kHz, mono)\n        async def send_audio(audio_bytes):\n            await ws.send(json.dumps({\n                \"type\": \"input_audio_buffer.append\",\n                \"audio\": base64.b64encode(audio_bytes).decode()\n            }))\n\n        # Receive events\n        async for message in ws:\n            event = json.loads(message)\n\n            if event[\"type\"] == \"resp\n```\n\n### Vapi Voice Agent\n\nBuild voice agents with Vapi platform\n\n**When to use**: Phone-based agents, quick deployment\n\n```python\n# Vapi provides hosted voice agents with webhooks\n\nfrom flask import Flask, request, jsonify\nimport vapi\n\napp = Flask(__name__)\nclient = vapi.Vapi(api_key=\"...\")\n\n# Create an assistant\nassistant = client.assistants.create(\n    name=\"Support Agent\",\n    model={\n        \"provider\": \"openai\",\n        \"model\": \"gpt-4o\",\n        \"messages\": [\n            {\n                \"role\": \"system\",\n                \"content\": \"You are a helpful support agent...\"\n            }\n        ]\n    },\n    voice={\n        \"provider\": \"11labs\",\n        \"voiceId\": \"21m00Tcm4TlvDq8ikWAM\"  # Rachel\n    },\n    firstMessage=\"Hi! How can I help you today?\",\n    transcriber={\n        \"provider\": \"deepgram\",\n        \"model\": \"nova-2\"\n    }\n)\n\n# Webhook for conversation events\n@app.route(\"\u002Fvapi\u002Fwebhook\", methods=[\"POST\"])\ndef vapi_webhook():\n    event = request.json\n\n    if event[\"type\"] == \"function-call\":\n        # Handle tool call\n        name = event[\"functionCall\"][\"name\"]\n        args = event[\"functionCall\"][\"parameters\"]\n\n        if name == \"check_order\":\n            result = check_order(args[\"order_id\"])\n            return jsonify({\"result\": result})\n\n    elif event[\"type\"] == \"end-of-call-report\":\n        # Call ended - save transcript\n        transcript = event[\"transcript\"]\n        save_transcript(event[\"call\"][\"id\"], transcript)\n\n    return jsonify({\"ok\": True})\n\n# Start outbound call\ncall = client.calls.create(\n    assistant_id=assistant.id,\n    customer={\n        \"number\": \"+1234567890\"\n    },\n    phoneNumber={\n        \"twilioPhoneNumber\": \"+0987654321\"\n    }\n)\n\n# Or create web call\nweb_call = client.calls.create(\n    assistant_id=assistant.id,\n    type=\"web\"\n)\n# Returns URL for WebRTC connection\n```\n\n### Deepgram STT + ElevenLabs TTS\n\nBest-in-class transcription and synthesis\n\n**When to use**: High quality voice, custom pipeline\n\n```python\nimport asyncio\nfrom deepgram import DeepgramClient, LiveTranscriptionEvents\nfrom elevenlabs import ElevenLabs\n\n# Deepgram real-time transcription\ndeepgram = DeepgramClient(api_key=\"...\")\n\nasync def transcribe_stream(audio_stream):\n    connection = deepgram.listen.live.v(\"1\")\n\n    async def on_transcript(result):\n        transcript = result.channel.alternatives[0].transcript\n        if transcript:\n            print(f\"Heard: {transcript}\")\n            if result.is_final:\n                # Process final transcript\n                await handle_user_input(transcript)\n\n    connection.on(LiveTranscriptionEvents.Transcript, on_transcript)\n\n    await connection.start({\n        \"model\": \"nova-2\",  # Best quality\n        \"language\": \"en\",\n        \"smart_format\": True,\n        \"interim_results\": True,  # Get partial results\n        \"utterance_end_ms\": 1000,\n        \"vad_events\": True,  # Voice activity detection\n        \"encoding\": \"linear16\",\n        \"sample_rate\": 16000\n    })\n\n    # Stream audio\n    async for chunk in audio_stream:\n        await connection.send(chunk)\n\n    await connection.finish()\n\n# ElevenLabs streaming synthesis\neleven = ElevenLabs(api_key=\"...\")\n\ndef text_to_speech_stream(text: str):\n    \"\"\"Stream TTS audio chunks.\"\"\"\n    audio_stream = eleven.text_to_speech.convert_as_stream(\n        voice_id=\"21m00Tcm4TlvDq8ikWAM\",  # Rachel\n        model_id=\"eleven_turbo_v2_5\",  # Fastest\n        text=text,\n        output_format=\"pcm_24000\"  # Raw PCM for low latency\n    )\n\n    for chunk in audio_stream:\n        yield chunk\n\n# Or with WebSocket for lowest latency\nasync def tts_websocket(text_stream):\n    async with eleven.text_to_speech.stream_async(\n        voice_id=\"21m00Tcm4TlvDq8ikWAM\",\n        model_id=\"eleven_turbo_v2_5\"\n    ) as tts:\n        async for text_chunk in text_stream:\n            audio = await tts.send(text_chunk)\n            yield audio\n\n        # Flush remaining audio\n        final_audio = await tts.flush()\n        yield final_audio\n```\n\n## Anti-Patterns\n\n### ❌ Non-streaming Pipeline\n\n**Why bad**: Adds seconds of latency.\nUser perceives as slow.\nLoses conversation flow.\n\n**Instead**: Stream everything:\n- STT: interim results\n- LLM: token streaming\n- TTS: chunk streaming\nStart TTS before LLM finishes.\n\n### ❌ Ignoring Interruptions\n\n**Why bad**: Frustrating user experience.\nFeels like talking to a machine.\nWastes time.\n\n**Instead**: Implement barge-in detection.\nUse VAD to detect user speech.\nStop TTS immediately.\nClear audio queue.\n\n### ❌ Single Provider Lock-in\n\n**Why bad**: May not be best quality.\nSingle point of failure.\nHarder to optimize.\n\n**Instead**: Mix best providers:\n- Deepgram for STT (speed + accuracy)\n- ElevenLabs for TTS (voice quality)\n- OpenAI\u002FAnthropic for LLM\n\n## Limitations\n\n- Latency varies by provider\n- Cost per minute adds up\n- Quality depends on network\n- Complex debugging\n\n## Related Skills\n\nWorks well with: `langgraph`, `structured-output`, `langfuse`\n",{"name":5,"source":54,"description":7},"vibeship-spawner-skills (Apache 2.0)","main","def8531e65114c0fca8fb8551c1871ee0eed705c","skills\u002Faiskillstore\u002Fmarketplace\u002Fsickn33\u002Fvoice-ai-development",24,"https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub","https:\u002F\u002Fskillshub.wtf","https:\u002F\u002Fgithub.com\u002FComeOnOliver","AI & Blockchain Engineer\r\n","2026-06-24 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