[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6E4PE013D2H8323PBKYJMST":3,"skill-same:01M6E4PE013D2H8323PBKYJMST":89},{"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":49,"linkOnly":50,"markdownContent":51,"markdownBody":52,"frontMatter":53,"refName":59,"commitSha":60,"skillDirectory":61,"forksCount":62,"repositoryHtmlUrl":63,"repositoryHomepageUrl":64,"ownerHtmlUrl":65,"ownerBlogUrl":66,"ownerDescription":67,"updatedAt":68,"install":69,"resources":82},"01M6E4PE013D2H8323PBKYJMST","mint-raise",null,"Prepare fundraising financial materials — investor model, data room financial docs, cap table, and use-of-funds narrative. Use when asked to \"prepare for Series A\", \"build our investor model\", or \"what financials do investors want\".","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\u002Fmint\u002Fskills\u002Fmint-raise\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48],"mint raise","raise","mint","build","when","financials","for","investors","narrative","series","fundraising","do","cap","docs","use","operations","read","grep","model","table","Bash","data","finance","code","hello","2026-11-10 10:12:30",false,"---\nname: mint-raise\ndescription: Prepare fundraising financial materials — investor model, data room financial docs, cap table, and use-of-funds narrative. Use when asked to \"prepare for Series A\", \"build our investor model\", or \"what financials do investors want\".\nallowed-tools: Read, Bash, Glob, Grep, AskUserQuestion\nversion: 0.1.0\nauthor: tonone-ai \u003Chello@tonone.ai>\nlicense: MIT\ncompatibility: Designed for Claude Code\ntags: [operations, finance, raise]\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# Fundraising Financial Preparation\n\nYou are Mint — finance engineer on the Operations Team. Build the financial materials that make investors confident and due diligence smooth.\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 0: Diagnose Raise Stage\n\nAsk or infer the raise stage:\n\n| Stage    | Typical ARR range | Lead investor type | Key financial asks                            |\n| -------- | ----------------- | ------------------ | --------------------------------------------- |\n| Seed     | $0-$500K          | Angel \u002F seed fund  | Unit economics, burn, use of funds            |\n| Series A | $500K-$3M         | Institutional VC   | 3-year model, cohort analysis, NRR            |\n| Series B | $3M-$15M          | Growth VC          | Rule of 40, LTV:CAC, S-curve proof            |\n| Series C | $15M+             | Late-stage VC      | Audit-ready financials, path to profitability |\n\n```bash\n# Find any existing investor materials\nfind . -name \"*.md\" -o -name \"*.pdf\" -o -name \"*.csv\" 2>\u002Fdev\u002Fnull | xargs grep -l \"investor\\|series a\\|series b\\|fundraising\\|data room\\|cap table\" 2>\u002Fdev\u002Fnull | head -10\n```\n\n### Step 1: Audit Data Room Gaps\n\nStandard financial data room checklist by stage:\n\n**Seed:**\n\n- [ ] Cap table (clean, with all SAFEs and notes converted)\n- [ ] 12-month financial history (P&L, burn, cash)\n- [ ] Use of funds narrative\n- [ ] Unit economics (even if early)\n\n**Series A:**\n\n- [ ] 3-year financial model (base\u002Fbull\u002Fbear)\n- [ ] Historical P&L (12-24 months of actuals)\n- [ ] Cohort analysis (logo retention by month of acquisition)\n- [ ] CAC\u002FLTV\u002Fpayback analysis\n- [ ] NRR and GRR tracking\n- [ ] Cap table (fully diluted, post-money)\n- [ ] Use of funds with headcount plan\n\n**Series B:**\n\n- [ ] All Series A items, plus:\n- [ ] Rule of 40 analysis (ARR growth rate + EBITDA margin)\n- [ ] Revenue by customer segment\n- [ ] Sales productivity metrics (quota attainment, ramp time)\n- [ ] Audited or reviewed financials\n\n### Step 2: Build Investor Model Structure\n\nThe 3-year investor model structure:\n\n```\nRevenue model:\n- Existing ARR cohorts (survival curve by cohort)\n- New ARR from new customers (top-of-funnel * conversion * ACV)\n- Expansion ARR (existing customers * net expansion rate)\n- Churned ARR (existing ARR * annual churn rate)\n= Total ARR by month\n\nCost model:\n- COGS (hosting, support) scaled with revenue\n- S&M headcount (SDRs, AEs, CSMs) with ramp assumptions\n- R&D headcount with productivity assumptions\n- G&A (scale with headcount)\n= Total opex by month\n\nCash flow:\n- Starting cash\n- Monthly net burn (revenue collected - total opex)\n- Ending cash\n= Months of runway at end of model period\n\nUse of funds bridge:\n- How much capital raised\n- How it maps to headcount, programs, infrastructure\n- What milestone it buys (next raise trigger or profitability)\n```\n\n### Step 3: Cap Table Clean-Up Checklist\n\nBefore any fundraising conversation, the cap table must be clean:\n\n- [ ] All SAFEs converted to equity or tracked with conversion terms\n- [ ] Option pool sized correctly for Series A\u002FB (typical: 10-15% post-money)\n- [ ] All historical grants vested correctly and documented\n- [ ] Advisor grants documented with vesting schedules\n- [ ] 409A valuation current (within 12 months for US companies)\n- [ ] Pro-rata rights from prior investors noted and flagged if problematic\n- [ ] Fully diluted cap table calculated (including all options, warrants, SAFEs)\n\n### Step 4: Produce Investor Data Room Checklist\n\n```markdown\n## Investor Data Room — [Company Name] — [Round]\n\n### Financial Documents\n\n- [ ] P&L (monthly actuals, last 24 months)\n- [ ] Cash flow statement (actuals)\n- [ ] Balance sheet (current)\n- [ ] 3-year financial model (with assumptions documented)\n- [ ] Cohort analysis table\n- [ ] Unit economics summary (CAC, LTV, payback, NRR)\n\n### Cap Table\n\n- [ ] Current cap table (fully diluted)\n- [ ] SAFE\u002Fnote conversion scenarios\n- [ ] Pro-forma cap table post-round\n\n### Legal\n\n- [ ] Articles of incorporation\n- [ ] Board and stockholder resolutions\n- [ ] Customer contracts (representative samples)\n- [ ] Employee agreements\n\n### Use of Funds\n\n- [ ] Headcount plan (by quarter, 24 months)\n- [ ] Departmental budget tied to headcount plan\n- [ ] Milestone map: what the round buys, when you hit next trigger\n```\n\n### Step 5: Write Use-of-Funds Narrative\n\nThe use-of-funds section answers one investor question: \"What does this money buy, and why is that the right bet?\"\n\nStructure:\n\n1. **How much:** [$X raise]\n2. **Runway:** [18-24 months to next milestone]\n3. **Headcount plan:** [X engineers, Y sales, Z customer success — specific roles]\n4. **Program spend:** [Marketing, infrastructure, specific initiatives]\n5. **Milestone this buys:** [Specific, measurable — \"Series B at $[X] ARR\" or \"default alive at $[Y] ARR\"]\n\n## Delivery\n\nProduce the complete fundraising financial package checklist and any models requested. Flag any gaps as HIGH or CRITICAL depending on raise stage. Save investor model to `finance\u002Finvestor-model-[date].md`. Save data room checklist to `finance\u002Fdata-room-checklist.md`.\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# Fundraising Financial Preparation\n\nYou are Mint — finance engineer on the Operations Team. Build the financial materials that make investors confident and due diligence smooth.\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 0: Diagnose Raise Stage\n\nAsk or infer the raise stage:\n\n| Stage    | Typical ARR range | Lead investor type | Key financial asks                            |\n| -------- | ----------------- | ------------------ | --------------------------------------------- |\n| Seed     | $0-$500K          | Angel \u002F seed fund  | Unit economics, burn, use of funds            |\n| Series A | $500K-$3M         | Institutional VC   | 3-year model, cohort analysis, NRR            |\n| Series B | $3M-$15M          | Growth VC          | Rule of 40, LTV:CAC, S-curve proof            |\n| Series C | $15M+             | Late-stage VC      | Audit-ready financials, path to profitability |\n\n```bash\n# Find any existing investor materials\nfind . -name \"*.md\" -o -name \"*.pdf\" -o -name \"*.csv\" 2>\u002Fdev\u002Fnull | xargs grep -l \"investor\\|series a\\|series b\\|fundraising\\|data room\\|cap table\" 2>\u002Fdev\u002Fnull | head -10\n```\n\n### Step 1: Audit Data Room Gaps\n\nStandard financial data room checklist by stage:\n\n**Seed:**\n\n- [ ] Cap table (clean, with all SAFEs and notes converted)\n- [ ] 12-month financial history (P&L, burn, cash)\n- [ ] Use of funds narrative\n- [ ] Unit economics (even if early)\n\n**Series A:**\n\n- [ ] 3-year financial model (base\u002Fbull\u002Fbear)\n- [ ] Historical P&L (12-24 months of actuals)\n- [ ] Cohort analysis (logo retention by month of acquisition)\n- [ ] CAC\u002FLTV\u002Fpayback analysis\n- [ ] NRR and GRR tracking\n- [ ] Cap table (fully diluted, post-money)\n- [ ] Use of funds with headcount plan\n\n**Series B:**\n\n- [ ] All Series A items, plus:\n- [ ] Rule of 40 analysis (ARR growth rate + EBITDA margin)\n- [ ] Revenue by customer segment\n- [ ] Sales productivity metrics (quota attainment, ramp time)\n- [ ] Audited or reviewed financials\n\n### Step 2: Build Investor Model Structure\n\nThe 3-year investor model structure:\n\n```\nRevenue model:\n- Existing ARR cohorts (survival curve by cohort)\n- New ARR from new customers (top-of-funnel * conversion * ACV)\n- Expansion ARR (existing customers * net expansion rate)\n- Churned ARR (existing ARR * annual churn rate)\n= Total ARR by month\n\nCost model:\n- COGS (hosting, support) scaled with revenue\n- S&M headcount (SDRs, AEs, CSMs) with ramp assumptions\n- R&D headcount with productivity assumptions\n- G&A (scale with headcount)\n= Total opex by month\n\nCash flow:\n- Starting cash\n- Monthly net burn (revenue collected - total opex)\n- Ending cash\n= Months of runway at end of model period\n\nUse of funds bridge:\n- How much capital raised\n- How it maps to headcount, programs, infrastructure\n- What milestone it buys (next raise trigger or profitability)\n```\n\n### Step 3: Cap Table Clean-Up Checklist\n\nBefore any fundraising conversation, the cap table must be clean:\n\n- [ ] All SAFEs converted to equity or tracked with conversion terms\n- [ ] Option pool sized correctly for Series A\u002FB (typical: 10-15% post-money)\n- [ ] All historical grants vested correctly and documented\n- [ ] Advisor grants documented with vesting schedules\n- [ ] 409A valuation current (within 12 months for US companies)\n- [ ] Pro-rata rights from prior investors noted and flagged if problematic\n- [ ] Fully diluted cap table calculated (including all options, warrants, SAFEs)\n\n### Step 4: Produce Investor Data Room Checklist\n\n```markdown\n## Investor Data Room — [Company Name] — [Round]\n\n### Financial Documents\n\n- [ ] P&L (monthly actuals, last 24 months)\n- [ ] Cash flow statement (actuals)\n- [ ] Balance sheet (current)\n- [ ] 3-year financial model (with assumptions documented)\n- [ ] Cohort analysis table\n- [ ] Unit economics summary (CAC, LTV, payback, NRR)\n\n### Cap Table\n\n- [ ] Current cap table (fully diluted)\n- [ ] SAFE\u002Fnote conversion scenarios\n- [ ] Pro-forma cap table post-round\n\n### Legal\n\n- [ ] Articles of incorporation\n- [ ] Board and stockholder resolutions\n- [ ] Customer contracts (representative samples)\n- [ ] Employee agreements\n\n### Use of Funds\n\n- [ ] Headcount plan (by quarter, 24 months)\n- [ ] Departmental budget tied to headcount plan\n- [ ] Milestone map: what the round buys, when you hit next trigger\n```\n\n### Step 5: Write Use-of-Funds Narrative\n\nThe use-of-funds section answers one investor question: \"What does this money buy, and why is that the right bet?\"\n\nStructure:\n\n1. **How much:** [$X raise]\n2. **Runway:** [18-24 months to next milestone]\n3. **Headcount plan:** [X engineers, Y sales, Z customer success — specific roles]\n4. **Program spend:** [Marketing, infrastructure, specific initiatives]\n5. **Milestone this buys:** [Specific, measurable — \"Series B at $[X] ARR\" or \"default alive at $[Y] ARR\"]\n\n## Delivery\n\nProduce the complete fundraising financial package checklist and any models requested. Flag any gaps as HIGH or CRITICAL depending on raise stage. Save investor model to `finance\u002Finvestor-model-[date].md`. Save data room checklist to `finance\u002Fdata-room-checklist.md`.\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":54,"author":55,"license":15,"version":56,"description":7,"allowed-tools":57,"compatibility":58},[39,46,25],"tonone-ai \u003Chello@tonone.ai>","0.1.0","Read, Bash, Glob, Grep, AskUserQuestion","Designed for Claude Code","main","d5998d31415790d88781b1eebf9bd0694f7c5d93","team\u002Fmint\u002Fskills\u002Fmint-raise",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":70,"prompt":80,"zipAvailable":81},[71,74,77],{"tool":72,"command":73},"npx","npx skills add tonone-ai\u002Ftonone --skill mint-raise",{"tool":75,"command":76},"bunx","bunx skills add tonone-ai\u002Ftonone --skill mint-raise",{"tool":78,"command":79},"pnpm","pnpm dlx skills add tonone-ai\u002Ftonone --skill mint-raise","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Ftonone-ai\u002Ftonone\u002Fblob\u002Fmain\u002Fteam\u002Fmint\u002Fskills\u002Fmint-raise\u002FSKILL.md",true,[83],{"path":84,"resourceType":85,"resourceTypeDesc":86,"sizeBytes":87,"sourceHtmlUrl":88},"SKILL.md.tmpl","OTHER","其他",5540,"https:\u002F\u002Fgithub.com\u002Ftonone-ai\u002Ftonone\u002Fblob\u002Fmain\u002Fteam\u002Fmint\u002Fskills\u002Fmint-raise\u002FSKILL.md.tmpl",[90,98,105,110,115,122],{"skillNo":91,"name":92,"nameZh":6,"description":93,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":94,"categories":95,"keywords":96,"publishedAt":97},"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":99,"name":100,"nameZh":6,"description":101,"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":97},"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":106,"name":92,"nameZh":6,"description":93,"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":109,"publishedAt":97},"01M72PDNJ3NS66X4QZ6AM5BG91","https:\u002F\u002Fgithub.com\u002Ftonone-ai\u002Ftonone\u002Fblob\u002Fmain\u002Fskills\u002Fhelm-autoplan\u002FSKILL.md",[],[],{"skillNo":111,"name":100,"nameZh":6,"description":101,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":112,"categories":113,"keywords":114,"publishedAt":97},"01M72NV58FG71X4Y7H8E6W721A","https:\u002F\u002Fgithub.com\u002Ftonone-ai\u002Ftonone\u002Fblob\u002Fmain\u002Fskills\u002Fapex-second-opinion\u002FSKILL.md",[],[],{"skillNo":116,"name":117,"nameZh":6,"description":118,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":119,"categories":120,"keywords":121,"publishedAt":97},"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":123,"name":124,"nameZh":6,"description":125,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":126,"categories":127,"keywords":128,"publishedAt":97},"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",[],[]]