[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6DZ7DSHT9DCE80WD5JFYRPM":3,"skill-same:01M6DZ7DSHT9DCE80WD5JFYRPM":71},{"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":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":50,"commitSha":51,"skillDirectory":52,"forksCount":14,"repositoryHtmlUrl":53,"repositoryHomepageUrl":54,"ownerHtmlUrl":55,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":56,"install":57,"resources":70},"01M6DZ7DSHT9DCE80WD5JFYRPM","voice-agents",null,"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","Aceryoung\u002Ffamilyproject","Aceryoung","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F75876477?v=4","01M6DY8Q14MHFC0JX3G14KK9CW","kkong","01M6DY8Q58SY27PE2AH6GFZP9Y",0,"MIT","https:\u002F\u002Fgithub.com\u002FAceryoung\u002Ffamilyproject\u002Fblob\u002Fmain\u002Fantigravity-awesome-skills-main\u002Fskills\u002Fvoice-agents\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"voice agents","voice","natural conversation","debug","interaction","systems","control","latency","conversation","challenge","just","speech recognition","tts","api","pipeline","flow","apache","speech","realtime api","realtime","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":5,"source":49,"description":7},"vibeship-spawner-skills (Apache 2.0)","main","483e87f6ad1460beac541f70200e988102e5c385","antigravity-awesome-skills-main\u002Fskills\u002Fvoice-agents","https:\u002F\u002Fgithub.com\u002FAceryoung\u002Ffamilyproject","https:\u002F\u002Ffamilyproject-iota.vercel.app","https:\u002F\u002Fgithub.com\u002FAceryoung","2026-09-04 09:36:44",{"source":8,"skillName":5,"commands":58,"prompt":68,"zipAvailable":69},[59,62,65],{"tool":60,"command":61},"npx","npx skills add Aceryoung\u002Ffamilyproject --skill voice-agents",{"tool":63,"command":64},"bunx","bunx skills add Aceryoung\u002Ffamilyproject --skill voice-agents",{"tool":66,"command":67},"pnpm","pnpm dlx skills add Aceryoung\u002Ffamilyproject --skill voice-agents","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FAceryoung\u002Ffamilyproject\u002Fblob\u002Fmain\u002Fantigravity-awesome-skills-main\u002Fskills\u002Fvoice-agents\u002FSKILL.md",true,[],[72,103,136,154,168,192],{"skillNo":73,"name":74,"nameZh":6,"description":75,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":76,"categories":77,"keywords":83,"publishedAt":44},"01M6DZ9PNJVNQ1M7196KMPEHZC","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\u002FAceryoung\u002Ffamilyproject\u002Fblob\u002Fmain\u002Fantigravity-awesome-skills-main\u002Fskills\u002Fzapier-make-patterns\u002FSKILL.md",[78],{"categoryNo":79,"categoryName":80,"taxonomyType":81,"taxonomyTypeDesc":82},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,40,100,101,102],"zapier make patterns","patterns","workflow","integrations","how to","apps","platform","but","build","complexity","power","simplicity","use","business","automate","how","code","automation","writing",{"skillNo":104,"name":105,"nameZh":6,"description":106,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":107,"categories":108,"keywords":115,"publishedAt":44},"01M6DZ9K3385QP8QC2F3ZHZH1W","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\u002FAceryoung\u002Ffamilyproject\u002Fblob\u002Fmain\u002Fantigravity-awesome-skills-main\u002Fskills\u002Fxss-html-injection\u002FSKILL.md",[109,112],{"categoryNo":110,"categoryName":111,"taxonomyType":81,"taxonomyTypeDesc":82},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":113,"categoryName":114,"taxonomyType":81,"taxonomyTypeDesc":82},"01JBSEEDCAT000000000000009","前端开发",[116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135],"html injection testing","Cross Site Scripting and HTML Injection Testing","injection","scripting","site","html","testing","vectors","exploit","applications","xss","cookies","content","web","test","steal","client","via","security","vulnerabilities",{"skillNo":137,"name":138,"nameZh":6,"description":139,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":140,"categories":141,"keywords":149,"publishedAt":44},"01M6DZ9B72D51Q21DTFD63TVEN","writing-skills","Use when creating new skills, editing existing skills, or verifying skills work before deployment","https:\u002F\u002Fgithub.com\u002FAceryoung\u002Ffamilyproject\u002Fblob\u002Fmain\u002Fantigravity-awesome-skills-main\u002Fskills\u002Fwriting-skills\u002FSKILL.md",[142,143,146],{"categoryNo":110,"categoryName":111,"taxonomyType":81,"taxonomyTypeDesc":82},{"categoryNo":144,"categoryName":145,"taxonomyType":81,"taxonomyTypeDesc":82},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":147,"categoryName":148,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000036","内容创作者",[102,150,151,96,152,153],"writing skills","deployment","editing","work",{"skillNo":155,"name":156,"nameZh":6,"description":157,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":158,"categories":159,"keywords":162,"publishedAt":44},"01M6DZ98HBB74FXZS78BATF6P5","writing-plans","Use when you have a spec or requirements for a multi-step task, before touching code","https:\u002F\u002Fgithub.com\u002FAceryoung\u002Ffamilyproject\u002Fblob\u002Fmain\u002Fantigravity-awesome-skills-main\u002Fskills\u002Fwriting-plans\u002FSKILL.md",[160,161],{"categoryNo":144,"categoryName":145,"taxonomyType":81,"taxonomyTypeDesc":82},{"categoryNo":147,"categoryName":148,"taxonomyType":21,"taxonomyTypeDesc":22},[163,102,164,165,166,96,100,167],"writing plans","plans","requirements","task","spec",{"skillNo":169,"name":170,"nameZh":6,"description":171,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":172,"categories":173,"keywords":175,"publishedAt":44},"01M6DZ94C3FCDXCTV795CTRP1V","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\u002FAceryoung\u002Ffamilyproject\u002Fblob\u002Fmain\u002Fantigravity-awesome-skills-main\u002Fskills\u002Fworkflow-automation\u002FSKILL.md",[174],{"categoryNo":79,"categoryName":80,"taxonomyType":81,"taxonomyTypeDesc":82},[176,101,86,177,39,178,40,179,180,181,182,183,184,185,186,187,188,189,190,191],"Workflow Automation","Production","network","workflows","accessibility","ai agents","execution","scripts","worker","payment","n8n","money","resume","orchestrator","parallel","inngest",{"skillNo":193,"name":194,"nameZh":6,"description":195,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":196,"categories":197,"keywords":199,"publishedAt":44},"01M6DZ90RQ8GCWE9CGPCMHMNDD","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\u002FAceryoung\u002Ffamilyproject\u002Fblob\u002Fmain\u002Fantigravity-awesome-skills-main\u002Fskills\u002Fwordpress-penetration-testing\u002FSKILL.md",[198],{"categoryNo":110,"categoryName":111,"taxonomyType":81,"taxonomyTypeDesc":82},[122,194,200,201,202,203,204,205,124,96,206,207,208,135,134],"penetration testing","wordpress","security assessment","sites","scan","pentest","user","plugins","assessment"]