[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CCEM7BVMB4QM1ETAZ6R22S":3,"skill-same:01M6CCEM7BVMB4QM1ETAZ6R22S":67},{"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":18,"publishedAt":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":45,"commitSha":46,"skillDirectory":47,"forksCount":14,"repositoryHtmlUrl":48,"repositoryHomepageUrl":6,"ownerHtmlUrl":49,"ownerBlogUrl":50,"ownerDescription":51,"updatedAt":52,"install":53,"resources":66},"01M6CCEM7BVMB4QM1ETAZ6R22S","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.","Gabriellmaia01\u002Fneuro-dose-assist","Gabriellmaia01","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F135177180?v=4","01M6CBRZEZC8G5C400AG3SPFNH","Gabriel","01M6CBRZK8MB7DWW0B4C82TV2G",0,"MIT","https:\u002F\u002Fgithub.com\u002FGabriellmaia01\u002Fneuro-dose-assist\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fskills\u002Fvoice-ai-development\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"development","voice ai development","voice","livekit","realtime","ready","use","text to speech","speech to text","text","how","api","applications","real-time","voice agents","Production","apache","speech","realtime api","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":44,"description":7},"vibeship-spawner-skills (Apache 2.0)","main","eac60cc6e0b6baa2a05a765a851fb10638f4e733",".agent\u002Fskills\u002Fskills\u002Fvoice-ai-development","https:\u002F\u002Fgithub.com\u002FGabriellmaia01\u002Fneuro-dose-assist","https:\u002F\u002Fgithub.com\u002FGabriellmaia01","https:\u002F\u002Fportfolio-bice-theta-81.vercel.app\u002F","💻 Desenvolvimento De Software_\r\n\r\n⚙️ Engenharia De Software\r\n","2026-11-12 08:51:23",{"source":8,"skillName":5,"commands":54,"prompt":64,"zipAvailable":65},[55,58,61],{"tool":56,"command":57},"npx","npx skills add Gabriellmaia01\u002Fneuro-dose-assist --skill voice-ai-development",{"tool":59,"command":60},"bunx","bunx skills add Gabriellmaia01\u002Fneuro-dose-assist --skill voice-ai-development",{"tool":62,"command":63},"pnpm","pnpm dlx skills add Gabriellmaia01\u002Fneuro-dose-assist --skill voice-ai-development","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FGabriellmaia01\u002Fneuro-dose-assist\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fskills\u002Fvoice-ai-development\u002FSKILL.md",true,[],[68,91,120,140,151,171],{"skillNo":69,"name":70,"nameZh":6,"description":71,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":72,"categories":73,"keywords":79,"publishedAt":39},"01M6CCFWA9JJ23708FSKK1QNBW","zapier-make-patterns","No-code automation democratizes workflow building. Zapier and Make (formerly Integromat) let non-developers automate business processes without writing code. But no-code doesn't mean no-complexity - these platforms have their own patterns, pitfalls, and breaking points.  This skill covers when to use which platform, how to build reliable automations, and when to graduate to code-based solutions. Key insight: Zapier optimizes for simplicity and integrations (7000+ apps), Make optimizes for power","https:\u002F\u002Fgithub.com\u002FGabriellmaia01\u002Fneuro-dose-assist\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fskills\u002Fzapier-make-patterns\u002FSKILL.md",[74],{"categoryNo":75,"categoryName":76,"taxonomyType":77,"taxonomyTypeDesc":78},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[80,81,82,83,84,35,29,85,86,87,25,88,89,90],"zapier make patterns","writing","workflow","automation","code","automate","integrations","business","simplicity","power","complexity",{"skillNo":92,"name":93,"nameZh":6,"description":94,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":95,"categories":96,"keywords":103,"publishedAt":39},"01M6CCFSBPM49S618W7G4NHH13","Cross-Site Scripting and HTML Injection Testing","This skill should be used when the user asks to \"test for XSS vulnerabilities\", \"perform cross-site scripting attacks\", \"identify HTML injection flaws\", \"exploit client-side injection vulnerabilities\", \"steal cookies via XSS\", or \"bypass content security policies\". It provides comprehensive techniques for detecting, exploiting, and understanding XSS and HTML injection attack vectors in web applications.","https:\u002F\u002Fgithub.com\u002FGabriellmaia01\u002Fneuro-dose-assist\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fskills\u002Fxss-html-injection\u002FSKILL.md",[97,100],{"categoryNo":98,"categoryName":99,"taxonomyType":77,"taxonomyTypeDesc":78},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":101,"categoryName":102,"taxonomyType":77,"taxonomyTypeDesc":78},"01JBSEEDCAT000000000000009","前端开发",[104,105,106,107,108,109,110,111,31,112,113,114,115,116,117,118,119],"injection","scripting","html","site","Cross Site Scripting and HTML Injection Testing","testing","html injection testing","cookies","xss","exploit","content","web","test","vulnerabilities","user","security",{"skillNo":121,"name":122,"nameZh":6,"description":123,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":124,"categories":125,"keywords":135,"publishedAt":39},"01M6CCFJ9XTPXHTE4JR06WY475","writing-skills","Use when creating new skills, editing existing skills, or verifying skills work before deployment","https:\u002F\u002Fgithub.com\u002FGabriellmaia01\u002Fneuro-dose-assist\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fskills\u002Fwriting-skills\u002FSKILL.md",[126,127,130],{"categoryNo":98,"categoryName":99,"taxonomyType":77,"taxonomyTypeDesc":78},{"categoryNo":128,"categoryName":129,"taxonomyType":77,"taxonomyTypeDesc":78},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":131,"categoryName":132,"taxonomyType":133,"taxonomyTypeDesc":134},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[81,136,137,138,139,25],"writing skills","deployment","work","editing",{"skillNo":141,"name":142,"nameZh":6,"description":143,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":144,"categories":145,"keywords":148,"publishedAt":39},"01M6CCFG98EMZZ43D1CPZZZ54E","writing-plans","Use when you have a spec or requirements for a multi-step task, before touching code","https:\u002F\u002Fgithub.com\u002FGabriellmaia01\u002Fneuro-dose-assist\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fskills\u002Fwriting-plans\u002FSKILL.md",[146,147],{"categoryNo":128,"categoryName":129,"taxonomyType":77,"taxonomyTypeDesc":78},{"categoryNo":131,"categoryName":132,"taxonomyType":133,"taxonomyTypeDesc":134},[81,149,150,84,25],"writing plans","spec",{"skillNo":152,"name":153,"nameZh":6,"description":154,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":155,"categories":156,"keywords":158,"publishedAt":39},"01M6CCFDPACVC9ZM8TN63MGBP5","workflow-automation","Workflow automation is the infrastructure that makes AI agents reliable. Without durable execution, a network hiccup during a 10-step payment flow means lost money and angry customers. With it, workflows resume exactly where they left off.  This skill covers the platforms (n8n, Temporal, Inngest) and patterns (sequential, parallel, orchestrator-worker) that turn brittle scripts into production-grade automation.  Key insight: The platforms make different tradeoffs. n8n optimizes for accessibility","https:\u002F\u002Fgithub.com\u002FGabriellmaia01\u002Fneuro-dose-assist\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fskills\u002Fworkflow-automation\u002FSKILL.md",[157],{"categoryNo":75,"categoryName":76,"taxonomyType":77,"taxonomyTypeDesc":78},[159,83,82,160,161,162,163,164,35,165,34,166,167,168,169,170],"Workflow Automation","network","ai agents","accessibility","workflows","infrastructure","flow","execution","inngest","parallel","orchestrator","resume",{"skillNo":172,"name":173,"nameZh":6,"description":174,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":175,"categories":176,"keywords":178,"publishedAt":39},"01M6CCFB0KAB0PPNV38XQPX6YX","WordPress Penetration Testing","This skill should be used when the user asks to \"pentest WordPress sites\", \"scan WordPress for vulnerabilities\", \"enumerate WordPress users, themes, or plugins\", \"exploit WordPress vulnerabilities\", or \"use WPScan\". It provides comprehensive WordPress security assessment methodologies.","https:\u002F\u002Fgithub.com\u002FGabriellmaia01\u002Fneuro-dose-assist\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fskills\u002Fwordpress-penetration-testing\u002FSKILL.md",[177],{"categoryNo":98,"categoryName":99,"taxonomyType":77,"taxonomyTypeDesc":78},[109,173,179,180,119,117,181,182,118,25,113,183,184],"penetration testing","wordpress","assessment","plugins","pentest","security assessment"]