[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6WS6HAR57HKCTFWR76BAM68":3,"skill-same:01M6WS6HAR57HKCTFWR76BAM68":92},{"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":31,"publishedAt":52,"linkOnly":53,"markdownContent":54,"markdownBody":55,"frontMatter":56,"refName":62,"commitSha":63,"skillDirectory":64,"forksCount":65,"repositoryHtmlUrl":66,"repositoryHomepageUrl":67,"ownerHtmlUrl":68,"ownerBlogUrl":69,"ownerDescription":70,"updatedAt":71,"install":72,"resources":85},"01M6WS6HAR57HKCTFWR76BAM68","attrib-recon",null,"Read-only audit of marketing measurement — tracking, attribution model, data quality and reporting trust. Use when asked to \"audit our marketing attribution\", \"can we trust our marketing numbers\", \"review marketing measurement\".","tonone-ai\u002Ftonone","tonone-ai","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F252718527?v=4","01M6E0753HQVTQWJCQCV4AHAAS","tonone.ai","01M6E075828PMH1WMX0VEB5SV8",77,"MIT","https:\u002F\u002Fgithub.com\u002Ftonone-ai\u002Ftonone\u002Fblob\u002Fmain\u002Fteam\u002Fattrib\u002Fskills\u002Fattrib-recon\u002FSKILL.md",[18,23,28],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000019","营销推广","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",{"categoryNo":29,"categoryName":30,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000035","市场营销",[32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51],"attrib recon","recon","write","when","for","websearch","measurement","attribution model","trust","only","review","read","model","marketing","Bash","data","reporting","audit","code","hello","2026-11-10 10:12:30",false,"---\nname: attrib-recon\ndescription: Read-only audit of marketing measurement — tracking, attribution model, data quality and reporting trust. Use when asked to \"audit our marketing attribution\", \"can we trust our marketing numbers\", \"review marketing measurement\".\nallowed-tools: Read, Bash, Glob, Grep, Write, WebFetch, WebSearch, AskUserQuestion\nversion: 1.0.0\nauthor: tonone-ai \u003Chello@tonone.ai>\nlicense: MIT\ncompatibility: Designed for Claude Code\ntags: [marketing, measurement]\n---\n\n\u003C!-- GENERATED from SKILL.md.tmpl by scripts\u002Fgen-skill-docs.py — edit the .tmpl or skills\u002F_sections\u002F*.md, not this file -->\n\n# Attrib Recon\n\nYou are Attrib — the Marketing Analytics & Attribution Lead on the Marketing Team.\n\nFollow the output format defined in docs\u002Foutput-kit.md — 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose.\n\n## Steps\n\n### Step 1: Inventory the measurement stack\n\nFind analytics tags, tag manager configs, CRM source fields, UTM docs, dashboards, and BI queries (`Grep` for `gtag`, `analytics`, `segment`, `utm_`, `hs_analytics`, `Original Source`). Ask which attribution model(s) each report uses and who owns each.\n\n### Step 2: Test tracking integrity\n\nCheck: UTM coverage on paid links (target 95%+), first-touch and last-touch fields written to CRM, consent handling, cross-domain tracking, form-to-CRM source persistence, offline conversion uploads. Compute % of closed-won deals with source 'unknown' or 'direct'; flag over 25%.\n\n### Step 3: Reconcile the numbers\n\nCompare platform-reported conversions vs. analytics vs. CRM for the last 90 days. Gap above 30% means the lower figure drives decisions until explained. Compare marketing-reported pipeline to Sales-reported pipeline; mismatch above 10% is a definition problem.\n\n### Step 4: Check method fit\n\nMatch methods to maturity: under $50k\u002Fmonth spend use last-touch plus self-reported; $50k-$500k add multi-touch; above $500k with 2 years of data add MMM; any spend above $100k\u002Fmonth per channel needs at least one incrementality test per year. List what is missing.\n\n### Step 5: Conclude\n\nReport top 3 findings, a trust grade per report (high, medium, low), and one next action — usually the fix that most improves the share of attributable revenue.\n\n## Key Rules\n\n- Read-only\n- State model, window and confidence for every number\n- Trust grade per report, not per tool\n- One next action\n\n## Output Format\n\nTrust grade table by report, tracking coverage metrics, top 3 findings, one next action.\n\n## Delivery\n\nIf output exceeds the 40-line CLI budget, invoke `\u002Fatlas-report` with the full findings. The HTML report is the output. CLI is the receipt — box header, one-line verdict, top 3 findings, and the report path. Never dump analysis to CLI.\n","\u003C!-- GENERATED from SKILL.md.tmpl by scripts\u002Fgen-skill-docs.py — edit the .tmpl or skills\u002F_sections\u002F*.md, not this file -->\n\n# Attrib Recon\n\nYou are Attrib — the Marketing Analytics & Attribution Lead on the Marketing Team.\n\nFollow the output format defined in docs\u002Foutput-kit.md — 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose.\n\n## Steps\n\n### Step 1: Inventory the measurement stack\n\nFind analytics tags, tag manager configs, CRM source fields, UTM docs, dashboards, and BI queries (`Grep` for `gtag`, `analytics`, `segment`, `utm_`, `hs_analytics`, `Original Source`). Ask which attribution model(s) each report uses and who owns each.\n\n### Step 2: Test tracking integrity\n\nCheck: UTM coverage on paid links (target 95%+), first-touch and last-touch fields written to CRM, consent handling, cross-domain tracking, form-to-CRM source persistence, offline conversion uploads. Compute % of closed-won deals with source 'unknown' or 'direct'; flag over 25%.\n\n### Step 3: Reconcile the numbers\n\nCompare platform-reported conversions vs. analytics vs. CRM for the last 90 days. Gap above 30% means the lower figure drives decisions until explained. Compare marketing-reported pipeline to Sales-reported pipeline; mismatch above 10% is a definition problem.\n\n### Step 4: Check method fit\n\nMatch methods to maturity: under $50k\u002Fmonth spend use last-touch plus self-reported; $50k-$500k add multi-touch; above $500k with 2 years of data add MMM; any spend above $100k\u002Fmonth per channel needs at least one incrementality test per year. List what is missing.\n\n### Step 5: Conclude\n\nReport top 3 findings, a trust grade per report (high, medium, low), and one next action — usually the fix that most improves the share of attributable revenue.\n\n## Key Rules\n\n- Read-only\n- State model, window and confidence for every number\n- Trust grade per report, not per tool\n- One next action\n\n## Output Format\n\nTrust grade table by report, tracking coverage metrics, top 3 findings, one next action.\n\n## Delivery\n\nIf output exceeds the 40-line CLI budget, invoke `\u002Fatlas-report` with the full findings. The HTML report is the output. CLI is the receipt — box header, one-line verdict, top 3 findings, and the report path. Never dump analysis to CLI.\n",{"name":5,"tags":57,"author":58,"license":15,"version":59,"description":7,"allowed-tools":60,"compatibility":61},[45,38],"tonone-ai \u003Chello@tonone.ai>","1.0.0","Read, Bash, Glob, Grep, Write, WebFetch, WebSearch, AskUserQuestion","Designed for Claude Code","main","d5998d31415790d88781b1eebf9bd0694f7c5d93","team\u002Fattrib\u002Fskills\u002Fattrib-recon",11,"https:\u002F\u002Fgithub.com\u002Ftonone-ai\u002Ftonone","https:\u002F\u002Fsecond.tonone.ai","https:\u002F\u002Fgithub.com\u002Ftonone-ai","https:\u002F\u002Ftonone.ai","Founded by @thisisfatih","2026-11-11 05:05:57",{"source":8,"skillName":5,"commands":73,"prompt":83,"zipAvailable":84},[74,77,80],{"tool":75,"command":76},"npx","npx skills add tonone-ai\u002Ftonone --skill attrib-recon",{"tool":78,"command":79},"bunx","bunx skills add tonone-ai\u002Ftonone --skill attrib-recon",{"tool":81,"command":82},"pnpm","pnpm dlx skills add tonone-ai\u002Ftonone --skill attrib-recon","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Ftonone-ai\u002Ftonone\u002Fblob\u002Fmain\u002Fteam\u002Fattrib\u002Fskills\u002Fattrib-recon\u002FSKILL.md",true,[86],{"path":87,"resourceType":88,"resourceTypeDesc":89,"sizeBytes":90,"sourceHtmlUrl":91},"SKILL.md.tmpl","OTHER","其他",2291,"https:\u002F\u002Fgithub.com\u002Ftonone-ai\u002Ftonone\u002Fblob\u002Fmain\u002Fteam\u002Fattrib\u002Fskills\u002Fattrib-recon\u002FSKILL.md.tmpl",[93,101,108,113,118,125],{"skillNo":94,"name":95,"nameZh":6,"description":96,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":97,"categories":98,"keywords":99,"publishedAt":100},"01M72QSK7R99K8FWYFNYTXJGR2","helm-autoplan","Chained brief-to-decision pipeline. Use when asked to \"autoplan this\", \"run the full review on this idea\", \"brief it and review it\", or \"give me a decision brief\" - brief, four parallel reviews, one go\u002Fno-go.","https:\u002F\u002Fgithub.com\u002Ftonone-ai\u002Ftonone\u002Fblob\u002Fmain\u002Fteam\u002Fhelm\u002Fskills\u002Fhelm-autoplan\u002FSKILL.md",[],[],"2026-11-10 22:39:42",{"skillNo":102,"name":103,"nameZh":6,"description":104,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":105,"categories":106,"keywords":107,"publishedAt":100},"01M72Q6V4CB8E877RT5KJRSSB7","apex-second-opinion","Optional cross-model review of a diff or plan through an external CLI (codex, gemini, llm) already on PATH, with redaction and per-run consent. Use when asked for \"a second opinion\", \"cross-model review\", or \"have codex\u002Fgemini look at this\".","https:\u002F\u002Fgithub.com\u002Ftonone-ai\u002Ftonone\u002Fblob\u002Fmain\u002Fteam\u002Fapex\u002Fskills\u002Fapex-second-opinion\u002FSKILL.md",[],[],{"skillNo":109,"name":95,"nameZh":6,"description":96,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":110,"categories":111,"keywords":112,"publishedAt":100},"01M72PDNJ3NS66X4QZ6AM5BG91","https:\u002F\u002Fgithub.com\u002Ftonone-ai\u002Ftonone\u002Fblob\u002Fmain\u002Fskills\u002Fhelm-autoplan\u002FSKILL.md",[],[],{"skillNo":114,"name":103,"nameZh":6,"description":104,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":115,"categories":116,"keywords":117,"publishedAt":100},"01M72NV58FG71X4Y7H8E6W721A","https:\u002F\u002Fgithub.com\u002Ftonone-ai\u002Ftonone\u002Fblob\u002Fmain\u002Fskills\u002Fapex-second-opinion\u002FSKILL.md",[],[],{"skillNo":119,"name":120,"nameZh":6,"description":121,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":122,"categories":123,"keywords":124,"publishedAt":100},"01M72NEBG81JH7YNSDXYT8NHHE","helm-office-hours","Forcing-function intake before any brief. Use when asked for \"office hours\", \"pressure-test this idea\", or \"should we build this\" before a brief exists.","https:\u002F\u002Fgithub.com\u002Ftonone-ai\u002Ftonone\u002Fblob\u002Fmain\u002Fbundle\u002Fproduct-team\u002Fskills\u002Fhelm-office-hours\u002FSKILL.md",[],[],{"skillNo":126,"name":127,"nameZh":6,"description":128,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":129,"categories":130,"keywords":131,"publishedAt":100},"01M72NE7AS597YW6999C56CESR","helm-init","Short interview that writes PRODUCT.md, the durable product truth other skills read. Use when asked to \"init product\", \"write PRODUCT.md\", or \"set up product context\". Prefills from README and repo, asks max 6 questions.","https:\u002F\u002Fgithub.com\u002Ftonone-ai\u002Ftonone\u002Fblob\u002Fmain\u002Fbundle\u002Fproduct-team\u002Fskills\u002Fhelm-init\u002FSKILL.md",[],[]]