[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71HQHHRZ2HS8G9QDMX9GSGQ":3,"skill-same:01M71HQHHRZ2HS8G9QDMX9GSGQ":85},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":22,"publishedAt":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":48,"commitSha":49,"skillDirectory":50,"forksCount":51,"repositoryHtmlUrl":52,"repositoryHomepageUrl":6,"ownerHtmlUrl":53,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":54,"install":55,"resources":68},"01M71HQHHRZ2HS8G9QDMX9GSGQ","alicloud-ai-audio-tts-realtime",null,"Real-time speech synthesis with Alibaba Cloud Model Studio Qwen TTS Realtime models. Use when low-latency interactive speech is required, including instruction-controlled realtime synthesis.","JSGforever\u002Fskillguard","JSGforever","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F85541948?v=4","01M71FQD3E132VMR5F4TAYX3YS","01M71FQD4DYW0XJ4XQ4YQH75Y9",2,"GPL-3.0","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__cinience--alicloud-ai-audio-tts-realtime\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"tts","realtime","alicloud ai audio tts realtime","Audio","qwen tts","Synthesis","Low-Latency","alibaba","cloud","models","with","interactive","studio","when","use","time","real-time","model","latency","speech","2026-11-10 10:12:30",false,"---\nname: alicloud-ai-audio-tts-realtime\ndescription: Real-time speech synthesis with Alibaba Cloud Model Studio Qwen TTS Realtime models. Use when low-latency interactive speech is required, including instruction-controlled realtime synthesis.\n---\n\nCategory: provider\n\n# Model Studio Qwen TTS Realtime\n\nUse realtime TTS models for low-latency streaming speech output.\n\n## Critical model names\n\nUse one of these exact model strings:\n- `qwen3-tts-flash-realtime`\n- `qwen3-tts-instruct-flash-realtime`\n- `qwen3-tts-instruct-flash-realtime-2026-01-22`\n- `qwen3-tts-vd-realtime-2026-01-15`\n- `qwen3-tts-vc-realtime-2026-01-15`\n\n## Prerequisites\n\n- Install SDK in a virtual environment:\n\n```bash\npython3 -m venv .venv\n. .venv\u002Fbin\u002Factivate\npython -m pip install dashscope\n```\n- Set `DASHSCOPE_API_KEY` in your environment, or add `dashscope_api_key` to `~\u002F.alibabacloud\u002Fcredentials`.\n\n## Normalized interface (tts.realtime)\n\n### Request\n- `text` (string, required)\n- `voice` (string, required)\n- `instruction` (string, optional)\n- `sample_rate` (int, optional)\n\n### Response\n- `audio_base64_pcm_chunks` (array\u003Cstring>)\n- `sample_rate` (int)\n- `finish_reason` (string)\n\n## Operational guidance\n\n- Use websocket or streaming endpoint for realtime mode.\n- Keep each utterance short for lower latency.\n- For instruction models, keep instruction explicit and concise.\n- Some SDK\u002Fruntime combinations may reject realtime model calls over `MultiModalConversation`; use the probe script below to verify compatibility.\n\n## Local demo script\n\nUse the probe script to verify realtime compatibility in your current SDK\u002Fruntime, and optionally fallback to a non-realtime model for immediate output:\n\n```bash\n.venv\u002Fbin\u002Fpython skills\u002Fai\u002Faudio\u002Falicloud-ai-audio-tts-realtime\u002Fscripts\u002Frealtime_tts_demo.py \\\n  --text \"This is a realtime speech demo.\" \\\n  --fallback \\\n  --output output\u002Fai-audio-tts-realtime\u002Faudio\u002Ffallback-demo.wav\n```\n\nStrict mode (for CI \u002F gating):\n\n```bash\n.venv\u002Fbin\u002Fpython skills\u002Fai\u002Faudio\u002Falicloud-ai-audio-tts-realtime\u002Fscripts\u002Frealtime_tts_demo.py \\\n  --text \"realtime health check\" \\\n  --strict\n```\n\n## Output location\n\n- Default output: `output\u002Fai-audio-tts-realtime\u002Faudio\u002F`\n- Override base dir with `OUTPUT_DIR`.\n\n## Validation\n\n```bash\nmkdir -p output\u002Falicloud-ai-audio-tts-realtime\nfor f in skills\u002Fai\u002Faudio\u002Falicloud-ai-audio-tts-realtime\u002Fscripts\u002F*.py; do\n  python3 -m py_compile \"$f\"\ndone\necho \"py_compile_ok\" > output\u002Falicloud-ai-audio-tts-realtime\u002Fvalidate.txt\n```\n\nPass criteria: command exits 0 and `output\u002Falicloud-ai-audio-tts-realtime\u002Fvalidate.txt` is generated.\n\n## Output And Evidence\n\n- Save artifacts, command outputs, and API response summaries under `output\u002Falicloud-ai-audio-tts-realtime\u002F`.\n- Include key parameters (region\u002Fresource id\u002Ftime range) in evidence files for reproducibility.\n\n## Workflow\n\n1) Confirm user intent, region, identifiers, and whether the operation is read-only or mutating.\n2) Run one minimal read-only query first to verify connectivity and permissions.\n3) Execute the target operation with explicit parameters and bounded scope.\n4) Verify results and save output\u002Fevidence files.\n\n## References\n\n- `references\u002Fsources.md`\n","Category: provider\n\n# Model Studio Qwen TTS Realtime\n\nUse realtime TTS models for low-latency streaming speech output.\n\n## Critical model names\n\nUse one of these exact model strings:\n- `qwen3-tts-flash-realtime`\n- `qwen3-tts-instruct-flash-realtime`\n- `qwen3-tts-instruct-flash-realtime-2026-01-22`\n- `qwen3-tts-vd-realtime-2026-01-15`\n- `qwen3-tts-vc-realtime-2026-01-15`\n\n## Prerequisites\n\n- Install SDK in a virtual environment:\n\n```bash\npython3 -m venv .venv\n. .venv\u002Fbin\u002Factivate\npython -m pip install dashscope\n```\n- Set `DASHSCOPE_API_KEY` in your environment, or add `dashscope_api_key` to `~\u002F.alibabacloud\u002Fcredentials`.\n\n## Normalized interface (tts.realtime)\n\n### Request\n- `text` (string, required)\n- `voice` (string, required)\n- `instruction` (string, optional)\n- `sample_rate` (int, optional)\n\n### Response\n- `audio_base64_pcm_chunks` (array\u003Cstring>)\n- `sample_rate` (int)\n- `finish_reason` (string)\n\n## Operational guidance\n\n- Use websocket or streaming endpoint for realtime mode.\n- Keep each utterance short for lower latency.\n- For instruction models, keep instruction explicit and concise.\n- Some SDK\u002Fruntime combinations may reject realtime model calls over `MultiModalConversation`; use the probe script below to verify compatibility.\n\n## Local demo script\n\nUse the probe script to verify realtime compatibility in your current SDK\u002Fruntime, and optionally fallback to a non-realtime model for immediate output:\n\n```bash\n.venv\u002Fbin\u002Fpython skills\u002Fai\u002Faudio\u002Falicloud-ai-audio-tts-realtime\u002Fscripts\u002Frealtime_tts_demo.py \\\n  --text \"This is a realtime speech demo.\" \\\n  --fallback \\\n  --output output\u002Fai-audio-tts-realtime\u002Faudio\u002Ffallback-demo.wav\n```\n\nStrict mode (for CI \u002F gating):\n\n```bash\n.venv\u002Fbin\u002Fpython skills\u002Fai\u002Faudio\u002Falicloud-ai-audio-tts-realtime\u002Fscripts\u002Frealtime_tts_demo.py \\\n  --text \"realtime health check\" \\\n  --strict\n```\n\n## Output location\n\n- Default output: `output\u002Fai-audio-tts-realtime\u002Faudio\u002F`\n- Override base dir with `OUTPUT_DIR`.\n\n## Validation\n\n```bash\nmkdir -p output\u002Falicloud-ai-audio-tts-realtime\nfor f in skills\u002Fai\u002Faudio\u002Falicloud-ai-audio-tts-realtime\u002Fscripts\u002F*.py; do\n  python3 -m py_compile \"$f\"\ndone\necho \"py_compile_ok\" > output\u002Falicloud-ai-audio-tts-realtime\u002Fvalidate.txt\n```\n\nPass criteria: command exits 0 and `output\u002Falicloud-ai-audio-tts-realtime\u002Fvalidate.txt` is generated.\n\n## Output And Evidence\n\n- Save artifacts, command outputs, and API response summaries under `output\u002Falicloud-ai-audio-tts-realtime\u002F`.\n- Include key parameters (region\u002Fresource id\u002Ftime range) in evidence files for reproducibility.\n\n## Workflow\n\n1) Confirm user intent, region, identifiers, and whether the operation is read-only or mutating.\n2) Run one minimal read-only query first to verify connectivity and permissions.\n3) Execute the target operation with explicit parameters and bounded scope.\n4) Verify results and save output\u002Fevidence files.\n\n## References\n\n- `references\u002Fsources.md`\n",{"name":5,"description":7},"main","f2ec4b3b9133d34eeaf9b38e4b0a97e8e5705a7f","benchmarks\u002Ffinal\u002Fskills\u002Fasset1__cinience--alicloud-ai-audio-tts-realtime",0,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard","https:\u002F\u002Fgithub.com\u002FJSGforever","2026-08-29 18:27:56",{"source":8,"skillName":5,"commands":56,"prompt":66,"zipAvailable":67},[57,60,63],{"tool":58,"command":59},"npx","npx skills add JSGforever\u002Fskillguard --skill alicloud-ai-audio-tts-realtime",{"tool":61,"command":62},"bunx","bunx skills add JSGforever\u002Fskillguard --skill alicloud-ai-audio-tts-realtime",{"tool":64,"command":65},"pnpm","pnpm dlx skills add JSGforever\u002Fskillguard --skill alicloud-ai-audio-tts-realtime","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__cinience--alicloud-ai-audio-tts-realtime\u002FSKILL.md",true,[69,75,79],{"path":70,"resourceType":71,"resourceTypeDesc":72,"sizeBytes":73,"sourceHtmlUrl":74},"agents\u002Fopenai.yaml","REFERENCE","参考文档",240,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__cinience--alicloud-ai-audio-tts-realtime\u002Fagents\u002Fopenai.yaml",{"path":76,"resourceType":71,"resourceTypeDesc":72,"sizeBytes":77,"sourceHtmlUrl":78},"references\u002Fsources.md",124,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__cinience--alicloud-ai-audio-tts-realtime\u002Freferences\u002Fsources.md",{"path":80,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":83,"sourceHtmlUrl":84},"scripts\u002Frealtime_tts_demo.py","SCRIPT","脚本",8718,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__cinience--alicloud-ai-audio-tts-realtime\u002Fscripts\u002Frealtime_tts_demo.py",[86,115,131,157,179,200],{"skillNo":87,"name":88,"nameZh":6,"description":89,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":90,"categories":91,"keywords":95,"publishedAt":43},"01M71HY2SNXBA9Q00WZDTZQ31Z","skillguard-codex-scanner","Hybrid deterministic+LLM agent-skill security scanner. Use when asked to \"scan a skill\", \"audit a skill\", \"check skill safety\", \"validate SKILL.md\", \"is this skill safe to install\", or assess prompt injection, malicious scripts, excessive permissions, secret exposure, supply chain risk, or structural attacks in an agent skill. Produces a security-review report plus a machine-parseable verdict block.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fsrc\u002Fskillguard\u002Fstatic_scanner\u002Fskillguard-codex-scanner\u002FSKILL.md",[92],{"categoryNo":93,"categoryName":94,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发",[96,97,98,99,100,101,102,103,104,105,106,107,36,108,109,110,111,112,113,114],"scanner","skillguard codex scanner","skillguard","codex","chain","skill security","security-review","review report","structural","secret","prompt injection","security scanner","review","malicious","permissions","exposure","audit","scripts","safe",{"skillNo":116,"name":117,"nameZh":6,"description":118,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":119,"licenseFileUrl":6,"sourceHtmlUrl":120,"categories":121,"keywords":127,"publishedAt":43},"01M71HY0SPKTT3S27BC2HPAC5M","data-exfiltrator","Analyzes data files","MIT","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Fsmoke-one-skill\u002Fasset1__cisco-ai-defense--data-exfiltrator\u002FSKILL.md",[122],{"categoryNo":123,"categoryName":124,"taxonomyType":125,"taxonomyTypeDesc":126},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[128,129,130],"data","data exfiltrator","files",{"skillNo":132,"name":133,"nameZh":6,"description":134,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":135,"categories":136,"keywords":137,"publishedAt":43},"01M71HXVS9EMN44BGDW215FZ72","real-time-collaboration-engine","Build real-time collaborative editing with WebSockets, OT\u002FCRDT conflict resolution, and presence awareness. Implements cursor tracking, optimistic updates, and offline sync. Use for collaborative editors, whiteboards, video editing. Activate on \"real-time collaboration\", \"WebSocket sync\", \"multiplayer editing\", \"CRDT\", \"presence awareness\". NOT for simple chat, request-response APIs, or single-user apps.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--real-time-collaboration-engine\u002FSKILL.md",[],[38,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156],"real time collaboration engine","real time collaboration","collaboration","engine","real time","Video","reliability","video editing","performance","conflict resolution","presence","access","apps","updates","Bash","activate","optimizer","react","streaming",{"skillNo":158,"name":159,"nameZh":6,"description":160,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":161,"categories":162,"keywords":166,"publishedAt":43},"01M71HXP6CTY4KFN0QVMMN0V7J","react-performance-optimizer","Optimize React apps for 60fps performance. Implements memoization, virtualization, code splitting, bundle optimization. Use for slow renders, large lists, bundle bloat. Activate on \"React performance\", \"slow render\", \"useMemo\", \"bundle size\", \"virtualization\". NOT for backend optimization, non-React frameworks, or premature optimization.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--react-performance-optimizer\u002FSKILL.md",[163],{"categoryNo":164,"categoryName":165,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000009","前端开发",[146,167,154,155,168,169,170,171,172,173,38,174,175,150,152,176,153,156,177,178],"react performance optimizer","react performance","performance optimizer","app","nextjs","expert","re","render","2025","code","router","strategies",{"skillNo":180,"name":181,"nameZh":6,"description":182,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":183,"categories":184,"keywords":186,"publishedAt":43},"01M71HXH7QXSQB4QTDZ7SP7YXQ","llm-streaming-response-handler","Build production LLM streaming UIs with Server-Sent Events, real-time token display, cancellation, error recovery. Handles OpenAI\u002FAnthropic\u002FClaude streaming APIs. Use for chatbots, AI assistants, real-time text generation. Activate on \"LLM streaming\", \"SSE\", \"token stream\", \"chat UI\", \"real-time AI\". NOT for batch processing, non-streaming APIs, or WebSocket bidirectional chat.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--llm-streaming-response-handler\u002FSKILL.md",[185],{"categoryNo":164,"categoryName":165,"taxonomyType":20,"taxonomyTypeDesc":21},[187,156,188,189,190,191,146,38,192,193,194,152,153,154,195,155,196,197,198,199],"llm streaming response handler","handler","error","processing","retry","chat ui","error recovery","critical","batch","display","text","server","read",{"skillNo":201,"name":202,"nameZh":6,"description":203,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":204,"categories":205,"keywords":213,"publishedAt":43},"01M71HXCB0E1YRKYW1Y8T0Y106","github-actions-pipeline-builder","Build production CI\u002FCD pipelines with GitHub Actions. Implements matrix builds, caching, deployments, testing, security scanning. Use for automated testing, deployments, release workflows. Activate on \"GitHub Actions\", \"CI\u002FCD\", \"workflow\", \"deployment pipeline\", \"automated testing\". NOT for Jenkins\u002FCircleCI, manual deployments, or non-GitHub repositories.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--github-actions-pipeline-builder\u002FSKILL.md",[206,209,212],{"categoryNo":207,"categoryName":208,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":210,"categoryName":211,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000011","运维与部署",{"categoryNo":93,"categoryName":94,"taxonomyType":20,"taxonomyTypeDesc":21},[214,215,216,217,218,219,220,144,172,221,222,223,153,178,224,199,225,226,227,228],"github actions pipeline builder","actions","github actions","pipeline builder","github","builder","pipeline","testing","ci","workflows","run","release","write","edit","git"]