Financial Model Prompt — Claude Skill

Financial model
built on real
assumptions.

Structured prompt. Scenario-ready. Investor-calibrated.

Most startup models collapse at the first investor question. This skill builds the assumptions before the spreadsheet.

Financial Model Prompt is a Claude AI skill that generates assumption-rich financial model prompts for Excel or Google Sheets — calibrated to your business type, revenue model, and stage.

⚡ Replaces 6–8 hours of spreadsheet scoping — runs in under 10 minutes.
Get this skill — $19
Output preview — Financial Model Prompt
Stated Assumptions
Every number is explained before the model is built.
STATED ASSUMPTIONS — SaaS Subscription Model
Revenue
  Starting MRR: $12,000
  Monthly growth: 7% MoM, months 1–6; 4.5% months 7–18
  Churn rate: 2.2% monthly gross logo churn
  ARPU: $149/month (Pro tier, 80% of mix)
Unit Economics
  CAC: $390 blended · LTV: $3,200 · Payback: 2.6 months
Cash & Runway
  Opening cash: $520,000 · Net burn: ~$19,000/mo
  Runway (base): ~27 months
All assumptions listed before the model is built — investors read this first.
Model Prompt · Claude in Excel
Ready-to-paste prompt for Claude in Excel or SheetAI.
Build an 18-month monthly financial model for a B2B SaaS business using a subscription (MRR waterfall) revenue structure.

Create tabs: Assumptions (all inputs, blue cells) → Revenue Build (MRR waterfall: new + expansion − churn) → Expense Schedule → P&L Summary → Cash Flow → Runway → Scenarios (base / bull / bear) → KPI Dashboard.

Formulas reference the Assumptions tab only — never hardcode values in calc cells. Conditional formatting: cash balance turns red below 6 months of runway.
Specific enough for Claude in Excel, SheetAI, or RizzCalc to execute without follow-up questions.
Revenue Build Logic
MRR waterfall with exact row-by-row formula logic.
Revenue tab logic:
Row 1: Opening MRR (= prior month closing MRR)
Row 2: New MRR (= new customers × ARPU)
Row 3: Expansion MRR (= % of existing MRR)
Row 4: Churned MRR (= churn rate × opening MRR)
Row 5: Net New MRR (= R2 + R3 − R4)
Row 6: Closing MRR (= R1 + R5)
Row 7: ARR (= Closing MRR × 12)
KPIs: MRR, ARR, NRR, GRR, Churn %, CAC, LTV, LTV:CAC, Payback
Revenue logic block matched to your revenue model — SaaS, marketplace, e-commerce, services, or hybrid.
Scenario tabs · Base / Bull / Bear
Three scenarios investors expect at seed stage and beyond.
Base case: Assumptions as stated. Growth 7% → 4.5% MoM, 2.2% churn, $19k net burn.

Bull case: Growth rate +3%, churn −0.5%, CAC improves 15% by month 9.

Bear case: Growth rate −3%, CAC +30%, 1-month delay on all Q3 hires, churn +0.8%.

Scenario toggle pulls from a single Assumptions tab cell — no manual re-entry.
Scenario tab structure expected by seed and Series A investors as standard since 2025.
Full output
What you receive
✓ Stated Assumptions table — every number explained
✓ Ready-to-paste Financial Model Prompt (Excel / Sheets)
✓ Revenue Build Logic block matched to your revenue model
✓ Expense schedule — headcount ramp + opex categories
✓ Scenario parameters — base / bull / bear
✓ KPI dashboard spec — 6–8 metrics for your model type
✓ Formatting rules for Claude in Excel, SheetAI, RizzCalc
1 / 5
How it works

How does Financial Model Prompt work?

  1. Tell Claude what you need — the skill asks 2-3 narrowing questions specific to the task.
  2. The skill runs live research and structures the output before writing a single word.
  3. An anti-slop quality gate checks the draft, then delivers a ready-to-use result for $19.
What you get

Why this model
survives due diligence.

📐
Bottom-up logic. Revenue driven by funnel metrics, not TAM percentages.
Investor-grade
🔀
Stage-calibrated. Pre-seed, seed, and Series A get different model structures.
Stage-matched
⚙️
Works with Claude in Excel, SheetAI, and RizzCalc — or build it yourself.
AI-tool ready
📊
Scenario tab included. Base, bull, and bear — one Assumptions toggle.
3-scenario output
Who it's for

Built for anyone
doing fundraising.

✓
Founders building their first investor-ready model.
✓
Fractional CFOs scoping models for early-stage clients.
✓
Financial analysts structuring projections for a new business unit.
✓
Seed-stage teams preparing board or investor reporting.
Not what this is for
✕It generates the model prompt, not the finished spreadsheet — you still build or paste it into your tool of choice.
✕Complex 3-statement models (full balance sheet + income + cash flow) for late-stage or public companies.
✕Audit-ready or accountant-certified financials — this is planning and forecasting, not statutory reporting.

The complete guide to ai financial model prompts covers what makes a model built on assumptions versus one built on templates.

"I've reviewed hundreds of startup models. This skill produced an assumptions table that was cleaner than most Series A decks I see. Founders stop guessing and start defending numbers."
RM
Rohan M.
CFO, Series B startup · Nairobi · Legal/Biz Bundle
Get it now
Financial model
built right.
$19.

Instant download. Works with your free Claude account. 7-day refund if it's not what you expected.

Buy Financial Model Skill — $19 Legal/Biz Bundle — $45
Legal/Biz Bundle includes
NDA / Contract Draft
Terms of Service & Privacy Policy
Financial Model Prompt
Grant & Proposal Writing
Visa Application Cover Letter
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$45 for all 5 skills
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Quick answers

Do I need a paid Claude account for Financial Model Prompt?

No. The free Claude plan is enough to get started and see the skill working. A Pro plan unlocks longer outputs.

How do I install the Financial Model Prompt skill?

Download the .skill file, open Claude, go to Customize → Skills → Upload a skill, and drop the file in. It appears in your slash-command list immediately.

Can I edit or customise the output after I receive it?

Yes. The output is plain text you own and can edit freely. Most users tweak specific details or the call-to-action to fit their exact use case before sending or publishing.

How specific do my inputs need to be to get a good result?

The more specific your input, the more precise the output. The skill asks targeted questions to gather context — answering these in detail produces noticeably better results than generic one-line inputs.

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