[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6RVMG13BPACES816K5H9WAR":3,"skill-same:01M6RVMG13BPACES816K5H9WAR":93},{"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":52,"commitSha":53,"skillDirectory":54,"forksCount":13,"repositoryHtmlUrl":55,"repositoryHomepageUrl":6,"ownerHtmlUrl":56,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":57,"install":58,"resources":70},"01M6RVMG13BPACES816K5H9WAR","lead-intelligence",null,"AI-native lead intelligence and outreach pipeline. Replaces Apollo, Clay, and ZoomInfo with agent-powered signal scoring, mutual ranking, warm path discovery, source-derived voice modeling, and channel-specific outreach across email, LinkedIn, and X. Use when the user wants to find, qualify, and reach high-value contacts.","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\u002Flead-intelligence\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000005","邮件与沟通","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],"lead","intelligence","lead intelligence","qualify","when","signal","value","source","path","contacts","native","email","ranking","modeling","across","clay","scoring","ai-native","discovery","pipeline","2026-11-10 10:12:30",false,"---\nname: converge-loop\ndescription: Multi-round gap-analysis convergence loop — sweep a product across dimensions (UIUX, login, services, payments, system), fix what is fixable each round, re-audit, and stop when consecutive rounds find nothing new. Produces a categorized Chinese report. NOT a one-shot audit or the corrections-rules meta-audit (\u002Faudit).\nwhen_to_use: \"直到converge, 收敛循环, gap分析, 找出所有问题, loop审核, 一轮一轮查, 找不出新毛病, converge loop, gap analysis loop, keep fixing until converged\"\nuser_invocable: true\n---\n\n# Converge Loop\n\nDrive a product to convergence: repeated rounds of gap discovery across\nagreed dimensions, fixing reversible findings in-round, parking the rest\nwith recommendations, stopping when new-findings dry up. The executable\ninstructions live in `prompt.md`; this body is the Codex-facing summary.\n\nKey contracts:\n\n- Every finding carries evidence (file:line, URL, or log excerpt) and a\n  dimension × severity classification. Findings without evidence are dropped.\n- Fixable = reversible AND unambiguous. Those are fixed in-round, verified\n  by the project's real gate plus a live re-probe, and committed small.\n- Everything else is parked with a recommendation (`.claude\u002Fdecisions.md` \u002F\n  `.claude\u002Fblockers.md`), never silently dropped, never used to stop the loop.\n- Convergence is explicit: default stop rule is two consecutive rounds with\n  zero new P1\u002FP2 findings (cap: 8 rounds). The final report is in Chinese,\n  categorized by dimension and severity, listing fixed work with evidence,\n  parked items with reasons, and mechanism gaps (\"how would we know this\n  succeeded\" monitoring that does not exist).\n","# Converge Loop\n\nDrive a product to convergence: repeated rounds of gap discovery across\nagreed dimensions, fixing reversible findings in-round, parking the rest\nwith recommendations, stopping when new-findings dry up. The executable\ninstructions live in `prompt.md`; this body is the Codex-facing summary.\n\nKey contracts:\n\n- Every finding carries evidence (file:line, URL, or log excerpt) and a\n  dimension × severity classification. Findings without evidence are dropped.\n- Fixable = reversible AND unambiguous. Those are fixed in-round, verified\n  by the project's real gate plus a live re-probe, and committed small.\n- Everything else is parked with a recommendation (`.claude\u002Fdecisions.md` \u002F\n  `.claude\u002Fblockers.md`), never silently dropped, never used to stop the loop.\n- Convergence is explicit: default stop rule is two consecutive rounds with\n  zero new P1\u002FP2 findings (cap: 8 rounds). The final report is in Chinese,\n  categorized by dimension and severity, listing fixed work with evidence,\n  parked items with reasons, and mechanism gaps (\"how would we know this\n  succeeded\" monitoring that does not exist).\n",{"name":48,"description":49,"when_to_use":50,"user_invocable":51},"converge-loop","Multi-round gap-analysis convergence loop — sweep a product across dimensions (UIUX, login, services, payments, system), fix what is fixable each round, re-audit, and stop when consecutive rounds find nothing new. Produces a categorized Chinese report. NOT a one-shot audit or the corrections-rules meta-audit (\u002Faudit).","直到converge, 收敛循环, gap分析, 找出所有问题, loop审核, 一轮一轮查, 找不出新毛病, converge loop, gap analysis loop, keep fixing until converged",true,"main","60a901a00f46e71601f58727cb5661f8d3fb6d24",".claude\u002Fskills\u002Fecc\u002Flead-intelligence","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":48,"commands":59,"prompt":69,"zipAvailable":51},[60,63,66],{"tool":61,"command":62},"npx","npx skills add sakamoto-family-smile\u002Fagent_monorepo --skill converge-loop",{"tool":64,"command":65},"bunx","bunx skills add sakamoto-family-smile\u002Fagent_monorepo --skill converge-loop",{"tool":67,"command":68},"pnpm","pnpm dlx skills add sakamoto-family-smile\u002Fagent_monorepo --skill converge-loop","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fsakamoto-family-smile\u002Fagent_monorepo\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fecc\u002Flead-intelligence\u002FSKILL.md",[71,77,81,85,89],{"path":72,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":75,"sourceHtmlUrl":76},"agents\u002Fenrichment-agent.md","REFERENCE","参考文档",2319,"https:\u002F\u002Fgithub.com\u002Fsakamoto-family-smile\u002Fagent_monorepo\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fecc\u002Flead-intelligence\u002Fagents\u002Fenrichment-agent.md",{"path":78,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":79,"sourceHtmlUrl":80},"agents\u002Fmutual-mapper.md",2549,"https:\u002F\u002Fgithub.com\u002Fsakamoto-family-smile\u002Fagent_monorepo\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fecc\u002Flead-intelligence\u002Fagents\u002Fmutual-mapper.md",{"path":82,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":83,"sourceHtmlUrl":84},"agents\u002Foutreach-drafter.md",3065,"https:\u002F\u002Fgithub.com\u002Fsakamoto-family-smile\u002Fagent_monorepo\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fecc\u002Flead-intelligence\u002Fagents\u002Foutreach-drafter.md",{"path":86,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":87,"sourceHtmlUrl":88},"agents\u002Fsignal-scorer.md",2028,"https:\u002F\u002Fgithub.com\u002Fsakamoto-family-smile\u002Fagent_monorepo\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fecc\u002Flead-intelligence\u002Fagents\u002Fsignal-scorer.md",{"path":90,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":91,"sourceHtmlUrl":92},"prompt.md",3832,"https:\u002F\u002Fgithub.com\u002Fshenxingy\u002FClade\u002Fblob\u002Fmain\u002Fconfigs\u002Fskills\u002Fconverge-loop\u002Fprompt.md",[94,127,135,155,178,205],{"skillNo":95,"name":96,"nameZh":6,"description":97,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":98,"categories":99,"keywords":106,"publishedAt":43},"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",[100,103],{"categoryNo":101,"categoryName":102,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000009","前端开发",{"categoryNo":104,"categoryName":105,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000016","图像与多媒体",[107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126],"Video","remotion video","remotion video creation","creation","Remotion","Charts","Captions","Audio","3D","Composition","Animation","best practices","more","animations","domain-specific","domain","three","Rules","Lottie","for",{"skillNo":128,"name":129,"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":130,"categories":131,"keywords":132,"publishedAt":43},"01M6VCJ23NPECPSZJKGY691F75","hotcook-recipes","https:\u002F\u002Fgithub.com\u002Fsakamoto-family-smile\u002Fagent_monorepo\u002Fblob\u002Fmain\u002Fhotcook-agent\u002Fdata\u002Fskills\u002Fhotcook-recipes\u002FSKILL.md",[],[133,134],"hotcook recipes","recipes",{"skillNo":136,"name":137,"nameZh":6,"description":138,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":139,"categories":140,"keywords":149,"publishedAt":43},"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",[141,144],{"categoryNo":142,"categoryName":143,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000007","代码审查",{"categoryNo":145,"categoryName":146,"taxonomyType":147,"taxonomyTypeDesc":148},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[150,151,152,153,154],"code helper","helper","code","code review","review",{"skillNo":156,"name":157,"nameZh":6,"description":158,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":159,"categories":160,"keywords":167,"publishedAt":43},"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",[161,164],{"categoryNo":162,"categoryName":163,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":165,"categoryName":166,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发",[168,169,170,171,172,173,174,175,176,177],"research assistant","research","assistant","MCP","web","search","brave search","web research","brave","web research assistant",{"skillNo":179,"name":180,"nameZh":6,"description":181,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":182,"categories":183,"keywords":188,"publishedAt":43},"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",[184,187],{"categoryNo":185,"categoryName":186,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":145,"categoryName":146,"taxonomyType":147,"taxonomyTypeDesc":148},[189,190,191,27,192,193,126,194,195,33,196,197,198,199,200,201,202,203,173,204],"x api","api","with","limits","rate","threads","reading","posting","platform","patterns","api integration","twitter","use","auth","analytics","OAuth",{"skillNo":206,"name":207,"nameZh":6,"description":208,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":209,"categories":210,"keywords":212,"publishedAt":43},"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",[211],{"categoryNo":165,"categoryName":166,"taxonomyType":20,"taxonomyTypeDesc":21},[213,214,215,216,171,217,218,152,219,220,221,222,201,223,224,225,226,33,29,27],"workspace surface audit","workspace","audit","hooks","workflows","connectors","capabilities","harness","repo","environment","up","harness setup","active","env"]