AGENT SKILL

mint-raise

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".

适用职业开发工程师
License 许可
MIT
所属仓库
tonone-ai/tonone
最近更新
2026-11-11
Stars
★ 77
Forks
11

01 前言 Front Matter

name
mint-raise
tags
[ "operations", "finance", "raise" ]
author
tonone-ai <hello@tonone.ai>
license
MIT
version
0.1.0
description
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".
allowed-tools
Read, Bash, Glob, Grep, AskUserQuestion
compatibility
Designed for Claude Code

02 资源文件 1

03 SKILL.md 正文

<!-- GENERATED from SKILL.md.tmpl by scripts/gen-skill-docs.py — edit the .tmpl or skills/_sections/*.md, not this file -->

Fundraising Financial Preparation

You are Mint — finance engineer on the Operations Team. Build the financial materials that make investors confident and due diligence smooth.

Follow the output format defined in docs/output-kit.md — 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose.

Steps

Step 0: Diagnose Raise Stage

Ask or infer the raise stage:

Stage Typical ARR range Lead investor type Key financial asks
Seed $0-$500K Angel / seed fund Unit economics, burn, use of funds
Series A $500K-$3M Institutional VC 3-year model, cohort analysis, NRR
Series B $3M-$15M Growth VC Rule of 40, LTV:CAC, S-curve proof
Series C $15M+ Late-stage VC Audit-ready financials, path to profitability
# Find any existing investor materials
find . -name "*.md" -o -name "*.pdf" -o -name "*.csv" 2>/dev/null | xargs grep -l "investor\|series a\|series b\|fundraising\|data room\|cap table" 2>/dev/null | head -10

Step 1: Audit Data Room Gaps

Standard financial data room checklist by stage:

Seed:

  • [ ] Cap table (clean, with all SAFEs and notes converted)
  • [ ] 12-month financial history (P&L, burn, cash)
  • [ ] Use of funds narrative
  • [ ] Unit economics (even if early)

Series A:

  • [ ] 3-year financial model (base/bull/bear)
  • [ ] Historical P&L (12-24 months of actuals)
  • [ ] Cohort analysis (logo retention by month of acquisition)
  • [ ] CAC/LTV/payback analysis
  • [ ] NRR and GRR tracking
  • [ ] Cap table (fully diluted, post-money)
  • [ ] Use of funds with headcount plan

Series B:

  • [ ] All Series A items, plus:
  • [ ] Rule of 40 analysis (ARR growth rate + EBITDA margin)
  • [ ] Revenue by customer segment
  • [ ] Sales productivity metrics (quota attainment, ramp time)
  • [ ] Audited or reviewed financials

Step 2: Build Investor Model Structure

The 3-year investor model structure:

Revenue model:
- Existing ARR cohorts (survival curve by cohort)
- New ARR from new customers (top-of-funnel * conversion * ACV)
- Expansion ARR (existing customers * net expansion rate)
- Churned ARR (existing ARR * annual churn rate)
= Total ARR by month

Cost model:
- COGS (hosting, support) scaled with revenue
- S&M headcount (SDRs, AEs, CSMs) with ramp assumptions
- R&D headcount with productivity assumptions
- G&A (scale with headcount)
= Total opex by month

Cash flow:
- Starting cash
- Monthly net burn (revenue collected - total opex)
- Ending cash
= Months of runway at end of model period

Use of funds bridge:
- How much capital raised
- How it maps to headcount, programs, infrastructure
- What milestone it buys (next raise trigger or profitability)

Step 3: Cap Table Clean-Up Checklist

Before any fundraising conversation, the cap table must be clean:

  • [ ] All SAFEs converted to equity or tracked with conversion terms
  • [ ] Option pool sized correctly for Series A/B (typical: 10-15% post-money)
  • [ ] All historical grants vested correctly and documented
  • [ ] Advisor grants documented with vesting schedules
  • [ ] 409A valuation current (within 12 months for US companies)
  • [ ] Pro-rata rights from prior investors noted and flagged if problematic
  • [ ] Fully diluted cap table calculated (including all options, warrants, SAFEs)

Step 4: Produce Investor Data Room Checklist

## Investor Data Room — [Company Name] — [Round]

### Financial Documents

- [ ] P&L (monthly actuals, last 24 months)
- [ ] Cash flow statement (actuals)
- [ ] Balance sheet (current)
- [ ] 3-year financial model (with assumptions documented)
- [ ] Cohort analysis table
- [ ] Unit economics summary (CAC, LTV, payback, NRR)

### Cap Table

- [ ] Current cap table (fully diluted)
- [ ] SAFE/note conversion scenarios
- [ ] Pro-forma cap table post-round

### Legal

- [ ] Articles of incorporation
- [ ] Board and stockholder resolutions
- [ ] Customer contracts (representative samples)
- [ ] Employee agreements

### Use of Funds

- [ ] Headcount plan (by quarter, 24 months)
- [ ] Departmental budget tied to headcount plan
- [ ] Milestone map: what the round buys, when you hit next trigger

Step 5: Write Use-of-Funds Narrative

The use-of-funds section answers one investor question: "What does this money buy, and why is that the right bet?"

Structure:

  1. How much: [$X raise]
  2. Runway: [18-24 months to next milestone]
  3. Headcount plan: [X engineers, Y sales, Z customer success — specific roles]
  4. Program spend: [Marketing, infrastructure, specific initiatives]
  5. Milestone this buys: [Specific, measurable — "Series B at $[X] ARR" or "default alive at $[Y] ARR"]

Delivery

Produce 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/investor-model-[date].md. Save data room checklist to finance/data-room-checklist.md.

If output exceeds the 40-line CLI budget, invoke /atlas-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.

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