[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6ADZRXN3V2A428Y4TSXVCTZ":3,"skill-same:01M6ADZRXN3V2A428Y4TSXVCTZ":80},{"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":23,"publishedAt":49,"linkOnly":50,"markdownContent":51,"markdownBody":52,"frontMatter":53,"refName":56,"commitSha":57,"skillDirectory":58,"forksCount":59,"repositoryHtmlUrl":60,"repositoryHomepageUrl":61,"ownerHtmlUrl":62,"ownerBlogUrl":63,"ownerDescription":64,"updatedAt":65,"install":66,"resources":79},"01M6ADZRXN3V2A428Y4TSXVCTZ","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.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33481,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fvoice-ai-development\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48],"development","ai development","voice ai development","voice","Low-Latency","realtime","when","spawner","production-ready","how to","apps","time","webrtc","expert","ready","text","how","api","voice agents","Production","apache","speech","realtime api","openai","infrastructure","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.\"\nmetadata:\n  source: 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,"metadata":54,"description":7},{"source":55},"vibeship-spawner-skills (Apache 2.0)","main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fvoice-ai-development",3931,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates","https:\u002F\u002Faitmpl.com","https:\u002F\u002Fgithub.com\u002Fdavila7","https:\u002F\u002Fdanielavila.me","Building AI dev tools with LLMs","2026-11-14 06:14:46",{"source":8,"skillName":5,"commands":67,"prompt":77,"zipAvailable":78},[68,71,74],{"tool":69,"command":70},"npx","npx skills add davila7\u002Fclaude-code-templates --skill voice-ai-development",{"tool":72,"command":73},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill voice-ai-development",{"tool":75,"command":76},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill voice-ai-development","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fvoice-ai-development\u002FSKILL.md",true,[],[81,107,134,167,202,226],{"skillNo":82,"name":83,"nameZh":6,"description":84,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":85,"categories":86,"keywords":88,"publishedAt":49},"01M70NHBTQ8BV0A2SCAZXEPHCX","wealthville-pool-scoring","Score DeFi liquidity pools before providing liquidity. Returns a 0-100 pool score and an Enter\u002FHold\u002FExit\u002FReduce\u002FAvoid verdict with calibrated confidence for ~68,800 Solana pools and 575 EVM pools, plus a public miss-inclusive track record. Use when asked whether a pool is worth LPing into, which pools look best right now, or how accurate the scores have actually been.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fweb-data\u002Fwealthville-pool-scoring\u002FSKILL.md",[87],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[89,90,91,92,93,94,95,96,97,30,98,99,100,101,102,103,104,40,105,106],"scoring","pool","wealthville pool scoring","liquidity","which","for","exit","now","use","reduce","with","record","track","verdict","evm","solana","public","look",{"skillNo":108,"name":109,"nameZh":6,"description":110,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":111,"categories":112,"keywords":114,"publishedAt":49},"01M70MB6VR8ERJ4EM8D5KHBMEV","technical-debt-auditor","Scans a codebase and produces a quantified technical debt report with a prioritized remediation plan. Analyzes dead code, TODO\u002FFIXME\u002FHACK markers, dependency staleness, complexity hotspots, code duplication signals, test coverage gaps, unused exports, and outdated patterns. Use this skill when the user asks to audit their codebase for tech debt, wants a health check of their code quality, says things like \"where should I focus refactoring\", \"find code smells\", \"audit my codebase\", \"what technical debt do we have\", \"prioritize cleanup work\", or asks about code quality metrics. Also trigger when the user mentions \"code rot\", \"legacy cleanup\", \"refactoring priorities\", or wants to understand the overall health of a project before a major effort. Trigger even if the user doesn't say \"technical debt\" explicitly — any request to systematically assess code quality and identify improvement priorities belongs here.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Ftechnical-debt-auditor\u002FSKILL.md",[113],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[115,116,117,118,119,120,121,122,123,124,125,30,126,127,128,129,130,131,132,133],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":135,"name":136,"nameZh":6,"description":137,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":138,"categories":139,"keywords":149,"publishedAt":49},"01M70KPYH9QNHCNME5GJQGE22K","readonly-sql-guard","Make an agent's database access provably read-only. Use when an agent runs caller-supplied SQL against a production\u002FERP\u002Fanalytics database (a \"query\" MCP tool, text-to-SQL, an admin console), or when reviewing DB-access code for write\u002FDDL\u002Fside-effect\u002Ftransaction-escape risks. Checks what a statement CALLS, not just its shape.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdatabase\u002Freadonly-sql-guard\u002FSKILL.md",[140,143,144],{"categoryNo":141,"categoryName":142,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":145,"categoryName":146,"taxonomyType":147,"taxonomyTypeDesc":148},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[150,151,152,153,154,155,156,94,157,158,159,160,161,162,163,97,164,165,30,166],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"skillNo":168,"name":169,"nameZh":6,"description":170,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":171,"licenseFileUrl":172,"sourceHtmlUrl":173,"categories":174,"keywords":178,"publishedAt":49},"01M6AFY6F7CC2BBZP1QD6VTNVC","web-artifacts-builder","Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[175],{"categoryNo":176,"categoryName":177,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,94,201],"artifacts builder","web","web artifacts builder","artifacts","builder","state","routing","state management","component","css","frontend","ui","Tools","file","components","shadcn","react","Tailwind","suite","jsx","html","single-file","ui components",{"skillNo":203,"name":204,"nameZh":6,"description":205,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":171,"licenseFileUrl":206,"sourceHtmlUrl":207,"categories":208,"keywords":213,"publishedAt":49},"01M6AFWF33SFV9Z3SF4PEEE4BF","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[209,212],{"categoryNo":210,"categoryName":211,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":176,"categoryName":177,"taxonomyType":21,"taxonomyTypeDesc":22},[214,215,216,217,218,180,219,189,190,220,221,222,223,94,224,99,225],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":227,"name":228,"nameZh":6,"description":229,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":171,"licenseFileUrl":230,"sourceHtmlUrl":231,"categories":232,"keywords":240,"publishedAt":49},"01M6AF67Q67JJJ5V51JR2P52KA","discernment-nudge","After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FSKILL.md",[233,236,237],{"categoryNo":234,"categoryName":235,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":238,"categoryName":239,"taxonomyType":147,"taxonomyTypeDesc":148},"01JBSEEDCAT000000000000036","内容创作者",[241,242,243,244,245,246,247,248,182,249,192,126,250,251,252,253,254,162,255,30,256,257,258,259,260,40,261],"discernment nudge","nudge","interpretation","help","run","reasoning","data","writing","lookup","content","review","conversation","short","up","invoke","you","analysis","claims","argument","assemble","recommendations"]