[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6RTF9QX2RYYZ94PT43KCQYS":3,"skill-same:01M6RTF9QX2RYYZ94PT43KCQYS":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":30,"publishedAt":51,"linkOnly":52,"markdownContent":53,"markdownBody":54,"frontMatter":55,"refName":59,"commitSha":60,"skillDirectory":61,"forksCount":13,"repositoryHtmlUrl":62,"repositoryHomepageUrl":6,"ownerHtmlUrl":63,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":64,"install":65,"resources":78},"01M6RTF9QX2RYYZ94PT43KCQYS","ai-regression-testing",null,"Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code.","sakamoto-family-smile\u002Fagent_monorepo","sakamoto-family-smile","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F132812403?v=4","01M6RTAT3H8PMWX5SJ65KP3VYF","01M6RTAV321WB5RAHCWTZMX5CV",0,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fsakamoto-family-smile\u002Fagent_monorepo\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fecc\u002Fai-regression-testing\u002FSKILL.md",[17,22,25],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000008","测试与质量","SCENARIO","使用场景",{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":26,"categoryName":27,"taxonomyType":28,"taxonomyTypeDesc":29},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],"regression testing","ai regression testing","regression","testing","api testing","catch","for","mode","reviews","strategies","patterns","spots","bug","model","api","database","code","development","workflows","sandbox","2026-11-10 10:12:30",false,"---\nname: voice-agents\ndescription: \"Voice agents represent the frontier of AI interaction - humans speaking naturally with AI systems. The challenge isn't just speech recognition and synthesis, it's achieving natural conversation flow with sub-800ms latency while handling interruptions, background noise, and emotional nuance.  This skill covers two architectures: speech-to-speech (OpenAI Realtime API, lowest latency, most natural) and pipeline (STT→LLM→TTS, more control, easier to debug). Key insight: latency is the constraint. Hu\"\nsource: vibeship-spawner-skills (Apache 2.0)\n---\n\n# Voice Agents\n\nYou are a voice AI architect who has shipped production voice agents handling\nmillions of calls. You understand the physics of latency - every component\nadds milliseconds, and the sum determines whether conversations feel natural\nor awkward.\n\nYour core insight: Two architectures exist. Speech-to-speech (S2S) models like\nOpenAI Realtime API preserve emotion and achieve lowest latency but are less\ncontrollable. Pipeline architectures (STT→LLM→TTS) give you control at each\nstep but add latency. Mos\n\n## Capabilities\n\n- voice-agents\n- speech-to-speech\n- speech-to-text\n- text-to-speech\n- conversational-ai\n- voice-activity-detection\n- turn-taking\n- barge-in-detection\n- voice-interfaces\n\n## Patterns\n\n### Speech-to-Speech Architecture\n\nDirect audio-to-audio processing for lowest latency\n\n### Pipeline Architecture\n\nSeparate STT → LLM → TTS for maximum control\n\n### Voice Activity Detection Pattern\n\nDetect when user starts\u002Fstops speaking\n\n## Anti-Patterns\n\n### ❌ Ignoring Latency Budget\n\n### ❌ Silence-Only Turn Detection\n\n### ❌ Long Responses\n\n## ⚠️ Sharp Edges\n\n| Issue | Severity | Solution |\n|-------|----------|----------|\n| Issue | critical | # Measure and budget latency for each component: |\n| Issue | high | # Target jitter metrics: |\n| Issue | high | # Use semantic VAD: |\n| Issue | high | # Implement barge-in detection: |\n| Issue | medium | # Constrain response length in prompts: |\n| Issue | medium | # Prompt for spoken format: |\n| Issue | medium | # Implement noise handling: |\n| Issue | medium | # Mitigate STT errors: |\n\n## Related Skills\n\nWorks well with: `agent-tool-builder`, `multi-agent-orchestration`, `llm-architect`, `backend`\n","# Voice Agents\n\nYou are a voice AI architect who has shipped production voice agents handling\nmillions of calls. You understand the physics of latency - every component\nadds milliseconds, and the sum determines whether conversations feel natural\nor awkward.\n\nYour core insight: Two architectures exist. Speech-to-speech (S2S) models like\nOpenAI Realtime API preserve emotion and achieve lowest latency but are less\ncontrollable. Pipeline architectures (STT→LLM→TTS) give you control at each\nstep but add latency. Mos\n\n## Capabilities\n\n- voice-agents\n- speech-to-speech\n- speech-to-text\n- text-to-speech\n- conversational-ai\n- voice-activity-detection\n- turn-taking\n- barge-in-detection\n- voice-interfaces\n\n## Patterns\n\n### Speech-to-Speech Architecture\n\nDirect audio-to-audio processing for lowest latency\n\n### Pipeline Architecture\n\nSeparate STT → LLM → TTS for maximum control\n\n### Voice Activity Detection Pattern\n\nDetect when user starts\u002Fstops speaking\n\n## Anti-Patterns\n\n### ❌ Ignoring Latency Budget\n\n### ❌ Silence-Only Turn Detection\n\n### ❌ Long Responses\n\n## ⚠️ Sharp Edges\n\n| Issue | Severity | Solution |\n|-------|----------|----------|\n| Issue | critical | # Measure and budget latency for each component: |\n| Issue | high | # Target jitter metrics: |\n| Issue | high | # Use semantic VAD: |\n| Issue | high | # Implement barge-in detection: |\n| Issue | medium | # Constrain response length in prompts: |\n| Issue | medium | # Prompt for spoken format: |\n| Issue | medium | # Implement noise handling: |\n| Issue | medium | # Mitigate STT errors: |\n\n## Related Skills\n\nWorks well with: `agent-tool-builder`, `multi-agent-orchestration`, `llm-architect`, `backend`\n",{"name":56,"source":57,"description":58},"voice-agents","vibeship-spawner-skills (Apache 2.0)","Voice agents represent the frontier of AI interaction - humans speaking naturally with AI systems. The challenge isn't just speech recognition and synthesis, it's achieving natural conversation flow with sub-800ms latency while handling interruptions, background noise, and emotional nuance.  This skill covers two architectures: speech-to-speech (OpenAI Realtime API, lowest latency, most natural) and pipeline (STT→LLM→TTS, more control, easier to debug). Key insight: latency is the constraint. Hu","main","efc7fb813cc2515f17c324642c83ac027cc02aee",".claude\u002Fskills\u002Fecc\u002Fai-regression-testing","https:\u002F\u002Fgithub.com\u002Fsakamoto-family-smile\u002Fagent_monorepo","https:\u002F\u002Fgithub.com\u002Fsakamoto-family-smile","2026-11-08 20:29:14",{"source":8,"skillName":56,"commands":66,"prompt":76,"zipAvailable":77},[67,70,73],{"tool":68,"command":69},"npx","npx skills add sakamoto-family-smile\u002Fagent_monorepo --skill voice-agents",{"tool":71,"command":72},"bunx","bunx skills add sakamoto-family-smile\u002Fagent_monorepo --skill voice-agents",{"tool":74,"command":75},"pnpm","pnpm dlx skills add sakamoto-family-smile\u002Fagent_monorepo --skill voice-agents","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fsakamoto-family-smile\u002Fagent_monorepo\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fecc\u002Fai-regression-testing\u002FSKILL.md",true,[79],{"path":80,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":83,"sourceHtmlUrl":84},".openskills.json","REFERENCE","参考文档",226,"https:\u002F\u002Fgithub.com\u002FAristidesAI\u002FVisionEmoji\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fvoice-agents\u002F.openskills.json",[86,118,126,141,164,189],{"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":98,"publishedAt":51},"01M6RW50THEFSKK42YNR8XFZA1","remotion-video-creation","Best practices for Remotion - Video creation in React. 29 domain-specific rules covering 3D, animations, audio, captions, charts, transitions, and more.","https:\u002F\u002Fgithub.com\u002Fsakamoto-family-smile\u002Fagent_monorepo\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fecc\u002Fremotion-video-creation\u002FSKILL.md",[92,95],{"categoryNo":93,"categoryName":94,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000009","前端开发",{"categoryNo":96,"categoryName":97,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000016","图像与多媒体",[99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,37],"Video","remotion video","remotion video creation","creation","Remotion","Charts","Captions","Audio","3D","Composition","Animation","best practices","more","animations","domain-specific","domain","three","Rules","Lottie",{"skillNo":119,"name":120,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":121,"categories":122,"keywords":123,"publishedAt":51},"01M6VCJ23NPECPSZJKGY691F75","hotcook-recipes","https:\u002F\u002Fgithub.com\u002Fsakamoto-family-smile\u002Fagent_monorepo\u002Fblob\u002Fmain\u002Fhotcook-agent\u002Fdata\u002Fskills\u002Fhotcook-recipes\u002FSKILL.md",[],[124,125],"hotcook recipes","recipes",{"skillNo":127,"name":128,"nameZh":6,"description":129,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":130,"categories":131,"keywords":136,"publishedAt":51},"01M6VCHHE00X5XHSDRD1KH1BW2","code-helper","Code review and assistance skill","https:\u002F\u002Fgithub.com\u002Fsakamoto-family-smile\u002Fagent_monorepo\u002Fblob\u002Fmain\u002Fagent-system-2\u002Fskills\u002Fcode-helper\u002Fskill.md",[132,135],{"categoryNo":133,"categoryName":134,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000007","代码审查",{"categoryNo":26,"categoryName":27,"taxonomyType":28,"taxonomyTypeDesc":29},[137,138,47,139,140],"code helper","helper","code review","review",{"skillNo":142,"name":143,"nameZh":6,"description":144,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":145,"categories":146,"keywords":153,"publishedAt":51},"01M6VCH3Q31A20H6SQ1H0J7JB2","research-assistant","Web research assistant skill using Brave Search MCP","https:\u002F\u002Fgithub.com\u002Fsakamoto-family-smile\u002Fagent_monorepo\u002Fblob\u002Fmain\u002Fagent-system-1\u002Fskills\u002Fresearch-assistant\u002Fskill.md",[147,150],{"categoryNo":148,"categoryName":149,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":151,"categoryName":152,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发",[154,155,156,157,158,159,160,161,162,163],"research assistant","research","assistant","MCP","web","search","brave search","web research","brave","web research assistant",{"skillNo":165,"name":166,"nameZh":6,"description":167,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":168,"categories":169,"keywords":172,"publishedAt":51},"01M6VCGZZGPNVJSCZ1JTMNZAHY","x-api","X\u002FTwitter API integration for posting tweets, threads, reading timelines, search, and analytics. Covers OAuth auth patterns, rate limits, and platform-native content posting. Use when the user wants to interact with X programmatically.","https:\u002F\u002Fgithub.com\u002Fsakamoto-family-smile\u002Fagent_monorepo\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fecc\u002Fx-api\u002FSKILL.md",[170,171],{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":26,"categoryName":27,"taxonomyType":28,"taxonomyTypeDesc":29},[173,45,174,175,176,177,37,178,179,180,181,182,41,183,184,185,186,187,159,188],"x api","with","when","limits","rate","threads","reading","native","posting","platform","api integration","twitter","use","auth","analytics","OAuth",{"skillNo":190,"name":191,"nameZh":6,"description":192,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":193,"categories":194,"keywords":196,"publishedAt":51},"01M6VCGVX6DGB1FKTA53QBBCQB","workspace-surface-audit","Audit the active repo, MCP servers, plugins, connectors, env surfaces, and harness setup, then recommend the highest-value ECC-native skills, hooks, agents, and operator workflows. Use when the user wants help setting up Claude Code or understanding what capabilities are actually available in their environment.","https:\u002F\u002Fgithub.com\u002Fsakamoto-family-smile\u002Fagent_monorepo\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fecc\u002Fworkspace-surface-audit\u002FSKILL.md",[195],{"categoryNo":151,"categoryName":152,"taxonomyType":20,"taxonomyTypeDesc":21},[197,198,199,200,157,49,201,47,202,203,204,205,185,206,207,208,209,180,210,175],"workspace surface audit","workspace","audit","hooks","connectors","capabilities","harness","repo","environment","up","harness setup","active","env","value"]