[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6WRV1Z5FZC17CD4F948SPJB":3,"skill-same:01M6WRV1Z5FZC17CD4F948SPJB":79},{"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":45,"linkOnly":46,"markdownContent":47,"markdownBody":48,"frontMatter":49,"refName":55,"commitSha":56,"skillDirectory":57,"forksCount":58,"repositoryHtmlUrl":59,"repositoryHomepageUrl":60,"ownerHtmlUrl":61,"ownerBlogUrl":62,"ownerDescription":63,"updatedAt":64,"install":65,"resources":78},"01M6WRV1Z5FZC17CD4F948SPJB","reach-model",null,"Build CAC, payback and channel budget models with marginal returns and pipeline-sourced targets. Use when asked to \"model our CAC\", \"calculate payback by channel\", \"how much pipeline can paid generate\".","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\u002Fskills\u002Freach-model\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44],"reach model","model","calculate","paid","cac-modeling","when","for","websearch","modeling","use","write","read","hello","returns","marketing","Bash","how","Budget","pipeline","code","generate","2026-11-10 10:12:30",false,"---\nname: reach-model\ndescription: Build CAC, payback and channel budget models with marginal returns and pipeline-sourced targets. Use when asked to \"model our CAC\", \"calculate payback by channel\", \"how much pipeline can paid generate\".\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, cac-modeling]\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# Reach Model\n\nYou are Reach — the Demand Generation & Paid Acquisition 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: Gather inputs\n\nNeed: target new ARR, ACV, win rate, sales cycle days, gross margin, channel history (spend, leads, conversion rates by stage). Reverse-solve: required pipeline = new ARR \u002F win rate. Required opportunities = pipeline \u002F average opportunity size.\n\n### Step 2: Build the funnel per channel\n\nRates by stage: click to lead, lead to MQL, MQL to SQL, SQL to opportunity, opportunity to won. Typical B2B: lead to MQL 30-50%, MQL to SQL 15-25%, SQL to opp 50-70%, opp to won 20-30%. Replace defaults with actuals when 100+ records exist. Compute cost per stage.\n\n### Step 3: Fit the response curve\n\nFit cost per opportunity as a function of spend: CPO(s) = a * s^b with b above 0 indicating diminishing returns. With fewer than 6 data points use a conservative multiplier: each doubling of spend raises marginal CPO 30-50%. Project marginal CPO at 1x, 1.5x, 2x current spend.\n\n### Step 4: Allocate to equalize marginal CAC\n\nGreedy allocation: add each increment to the channel with lowest marginal CAC until target pipeline is met or marginal CAC hits ceiling (LTV x margin \u002F 3). Report the residual gap that paid cannot fill.\n\n### Step 5: Output scenarios and payback\n\nThree scenarios (conservative, base, aggressive at -25%, base, +25% conversion). Payback months = CAC \u002F (monthly ARPA x gross margin). Flag any channel over 18 months payback. Stamp assumptions and confidence per channel.\n\n## Key Rules\n\n- Show assumptions next to every output\n- Marginal, not average, drives allocation\n- Lag results by one sales cycle before claiming payback\n- State the pipeline gap paid cannot fill rather than stretching assumptions\n\n## Output Format\n\nChannel model table (spend, marginal CAC, pipeline, payback), three scenarios, assumptions block, gap statement.\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# Reach Model\n\nYou are Reach — the Demand Generation & Paid Acquisition 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: Gather inputs\n\nNeed: target new ARR, ACV, win rate, sales cycle days, gross margin, channel history (spend, leads, conversion rates by stage). Reverse-solve: required pipeline = new ARR \u002F win rate. Required opportunities = pipeline \u002F average opportunity size.\n\n### Step 2: Build the funnel per channel\n\nRates by stage: click to lead, lead to MQL, MQL to SQL, SQL to opportunity, opportunity to won. Typical B2B: lead to MQL 30-50%, MQL to SQL 15-25%, SQL to opp 50-70%, opp to won 20-30%. Replace defaults with actuals when 100+ records exist. Compute cost per stage.\n\n### Step 3: Fit the response curve\n\nFit cost per opportunity as a function of spend: CPO(s) = a * s^b with b above 0 indicating diminishing returns. With fewer than 6 data points use a conservative multiplier: each doubling of spend raises marginal CPO 30-50%. Project marginal CPO at 1x, 1.5x, 2x current spend.\n\n### Step 4: Allocate to equalize marginal CAC\n\nGreedy allocation: add each increment to the channel with lowest marginal CAC until target pipeline is met or marginal CAC hits ceiling (LTV x margin \u002F 3). Report the residual gap that paid cannot fill.\n\n### Step 5: Output scenarios and payback\n\nThree scenarios (conservative, base, aggressive at -25%, base, +25% conversion). Payback months = CAC \u002F (monthly ARPA x gross margin). Flag any channel over 18 months payback. Stamp assumptions and confidence per channel.\n\n## Key Rules\n\n- Show assumptions next to every output\n- Marginal, not average, drives allocation\n- Lag results by one sales cycle before claiming payback\n- State the pipeline gap paid cannot fill rather than stretching assumptions\n\n## Output Format\n\nChannel model table (spend, marginal CAC, pipeline, payback), three scenarios, assumptions block, gap statement.\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":50,"author":51,"license":15,"version":52,"description":7,"allowed-tools":53,"compatibility":54},[38,28],"tonone-ai \u003Chello@tonone.ai>","1.0.0","Read, Bash, Glob, Grep, Write, WebFetch, WebSearch, AskUserQuestion","Designed for Claude Code","main","d5998d31415790d88781b1eebf9bd0694f7c5d93","skills\u002Freach-model",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":66,"prompt":76,"zipAvailable":77},[67,70,73],{"tool":68,"command":69},"npx","npx skills add tonone-ai\u002Ftonone --skill reach-model",{"tool":71,"command":72},"bunx","bunx skills add tonone-ai\u002Ftonone --skill reach-model",{"tool":74,"command":75},"pnpm","pnpm dlx skills add tonone-ai\u002Ftonone --skill reach-model","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Ftonone-ai\u002Ftonone\u002Fblob\u002Fmain\u002Fskills\u002Freach-model\u002FSKILL.md",true,[],[80,88,95,100,105,112],{"skillNo":81,"name":82,"nameZh":6,"description":83,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":84,"categories":85,"keywords":86,"publishedAt":87},"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":89,"name":90,"nameZh":6,"description":91,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":92,"categories":93,"keywords":94,"publishedAt":87},"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":96,"name":82,"nameZh":6,"description":83,"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":87},"01M72PDNJ3NS66X4QZ6AM5BG91","https:\u002F\u002Fgithub.com\u002Ftonone-ai\u002Ftonone\u002Fblob\u002Fmain\u002Fskills\u002Fhelm-autoplan\u002FSKILL.md",[],[],{"skillNo":101,"name":90,"nameZh":6,"description":91,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":102,"categories":103,"keywords":104,"publishedAt":87},"01M72NV58FG71X4Y7H8E6W721A","https:\u002F\u002Fgithub.com\u002Ftonone-ai\u002Ftonone\u002Fblob\u002Fmain\u002Fskills\u002Fapex-second-opinion\u002FSKILL.md",[],[],{"skillNo":106,"name":107,"nameZh":6,"description":108,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":109,"categories":110,"keywords":111,"publishedAt":87},"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":113,"name":114,"nameZh":6,"description":115,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":116,"categories":117,"keywords":118,"publishedAt":87},"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",[],[]]