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Use this skill when building financial models, DCF analyses, revenue forecasts, scenario analyses, or cap tables. Triggers on DCF, LBO, revenue forecasting, scenario analysis, cap tables, financial projections, valuation, unit economics, and any task requiring financial model design or analysis.
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| name | financial-modeling |
| version | 0.1.0 |
| description | Use this skill when building financial models, DCF analyses, revenue forecasts, scenario analyses, or cap tables. Triggers on DCF, LBO, revenue forecasting, scenario analysis, cap tables, financial projections, valuation, unit economics, and any task requiring financial model design or analysis. |
| tags | ["financial-modeling","dcf","valuation","forecasting","cap-table","analysis","experimental-design","sales","finance","simulation"] |
| category | operations |
| recommended_skills | ["budgeting-planning","financial-reporting","startup-fundraising","spreadsheet-modeling"] |
| platforms | ["claude-code","gemini-cli","openai-codex"] |
| license | MIT |
| maintainers | [{"github":"maddhruv"}] |
Assumptions drive everything - make them explicit - A model is only as good as its inputs. Every key assumption (growth rate, churn, gross margin) should live in a clearly labeled inputs section, not be buried in formulas. If you can't defend an assumption in 10 seconds, it's not ready.
Build for scenarios, not point estimates - A single-case model is a false sense of precision. Reality will land somewhere between your bear and bull cases. Structure every model with at least three scenarios from day one - it forces you to think about the range of outcomes, not just the hoped-for one.
Separate inputs, calculations, and outputs - Inputs (assumptions) belong in one section. Formulas (calculations) reference only inputs or other calculations. Outputs (charts, summaries) reference only calculations. Never hard-code a number in a formula that should be an assumption. This separation makes auditing and updating the model fast and safe.
Stress test the downside - Most financial models are too optimistic. Reverse- engineer the downside: "What churn rate makes this business unviable?" or "What growth rate do we need to hit break-even in 18 months?" Knowing the failure thresholds is more valuable than the base case.
The model is a tool, not the answer - A model produces a range, not a verdict. Use it to understand sensitivity, pressure-test logic, and communicate trade-offs. Never present a DCF output as a price target without showing the key sensitivities. The goal is better thinking, not false precision.
The foundation of any serious financial model. The three statements are interconnected:
| Statement | What it shows | Key link |
|---|---|---|
| Income statement | Revenue, costs, profit over a period | Net income flows to retained earnings |
| Balance sheet | Assets, liabilities, equity at a point in time | Cash from cash flow statement |
| Cash flow statement | Actual cash in/out, reconciles profit to cash | Starts from net income |
For most startup models, a simplified version suffices: revenue build, gross margin, operating expenses, and ending cash balance. Add the balance sheet and full cash flow statement when modeling working capital, debt, or M&A.
A DCF (Discounted Cash Flow) values a business by the present value of its future free cash flows. The mechanics:
PV = CF / (1 + r)^nThe terminal value typically represents 60-80% of DCF value. This makes the discount rate and terminal growth rate the two most important (and most uncertain) inputs.
Unit economics measure the profitability of a single customer or transaction:
(ARPU * Gross Margin %) / Churn RateCAC / (ARPU * Gross Margin %) - months to recover acquisition costA cap table tracks ownership in a company across all shareholders:
Pre-money + new investmentPre-money valuation / fully diluted shares outstandingStart from customer counts, not a top-down percentage. Bottoms-up is more defensible:
New customers per month = (Website visitors * conversion rate)
OR (SDR capacity * meeting rate * close rate)
Monthly Recurring Revenue (MRR):
Starting MRR
+ New MRR (new customers * ARPU)
+ Expansion MRR (upsells/upgrades)
- Churned MRR (prior MRR * churn rate)
= Ending MRR
ARR = Ending MRR * 12
Layer in gross margin (typically 60-80% for SaaS) to get gross profit. Model cohort-level retention to capture expansion revenue and logo churn separately.
Key assumption to stress test: monthly churn rate. At 2% monthly churn, you lose ~21% of revenue per year. At 5%, you lose ~46%. The business model changes entirely.
Enterprise Value = Sum(FCF / (1+r)^t) + TV / (1+r)^nEquity Value = Enterprise Value - Net DebtSanity check: implied revenue multiple at your DCF value vs current comps. If your DCF implies a 30x revenue multiple when comps trade at 8x, revisit your assumptions.
Build a cohort model to make unit economics concrete:
Inputs:
ARPU (monthly) = $500
Gross margin = 75%
Monthly churn = 2%
Blended CAC = $3,000
Calculations:
Average customer life = 1 / 2% = 50 months
LTV = $500 * 75% * 50 = $18,750
LTV:CAC ratio = $18,750 / $3,000 = 6.25x (healthy)
CAC payback period = $3,000 / ($500 * 75%) = 8 months (excellent)
Model the blended CAC separately by channel (paid, organic, sales) - blended CAC hides the efficiency differences between channels.
Scenario analysis is not sensitivity analysis. Scenarios change multiple assumptions together to tell a coherent story:
| Assumption | Bear Case | Base Case | Bull Case |
|---|---|---|---|
| Monthly growth rate | 5% | 12% | 20% |
| Monthly churn | 4% | 2% | 1% |
| Gross margin | 60% | 72% | 78% |
| Sales efficiency | 0.5x | 0.8x | 1.2x |
Build a single scenario toggle (a dropdown or input cell) that switches all assumptions at once. Never copy-paste a model three times - use one model with a scenario selector feeding the inputs section.
Track shares and ownership through each round:
Founding:
Founders: 8,000,000 shares = 100%
Seed round ($2M on $8M pre-money):
Pre-money valuation: $8,000,000
New shares issued: 2,000,000 (= $2M / ($8M / 8M shares))
Post-money valuation: $10,000,000
Post-money ownership:
Founders: 8M / 10M = 80%
Seed investors: 2M / 10M = 20%
With 10% option pool (created pre-money):
Pre-money shares: 8M founders + 889K options = 8,889K
Price per share: $8M / 8,889K = $0.90
New shares: $2M / $0.90 = 2,222K
Founders post: 8M / 11,111K = 72% (option pool diluted founders, not investors)
Build headcount-driven opex, not a percentage of revenue:
For each department (Eng, Sales, Marketing, G&A, CS):
Headcount plan (by month)
x Average fully-loaded cost per head (salary + benefits + equipment ~1.25x base)
= Headcount expense
+ Non-headcount budget (tools, contractors, marketing spend)
= Total department expense
Sum all departments for total opex. Overlay on gross profit to get EBITDA and cash burn. Always model month-end headcount, not average - hiring lag matters.
Use two-variable data tables to visualize how the outcome changes across key inputs:
Example: IRR sensitivity to entry multiple and exit multiple
Exit Multiple
6x 8x 10x 12x
Entry 4x | 22% | 35% | 46% | 56%
Multi 6x | 8% | 19% | 29% | 38%
8x | -2% | 8% | 17% | 25%
10x | -9% | 0% | 8% | 16%
Always pick the two inputs with the highest impact on your output for the table. For a DCF, that is almost always discount rate vs terminal growth rate, or discount rate vs exit multiple.
| Anti-pattern | Why it's wrong | What to do instead |
|---|---|---|
| Hard-coding numbers in formulas | Model becomes impossible to audit or update | All assumptions in a labeled inputs section; formulas reference inputs |
| Single-point forecast | Creates false precision, hides risk | Build three scenarios minimum; show a range |
| Top-down revenue forecast ("we'll capture 1% of a $10B market") | Untestable, disconnected from reality | Bottoms-up from unit economics and customer acquisition drivers |
| Ignoring churn in a SaaS model | Overstates long-run revenue dramatically | Model cohort-level retention, separate logo vs revenue churn |
| Using pre-money option pool in cap table wrong | Underestimates founder dilution | Model option pool shuffle explicitly; show pre vs post ownership for each party |
| Confusing cash profit with accounting profit | Profitable companies go bankrupt from cash timing | Always include a cash flow / burn schedule; track change in working capital |
Terminal value represents 60-80% of DCF value - small changes to terminal growth rate or discount rate swing valuation by 30-50% - This makes the DCF highly sensitive to two of its most uncertain inputs. Always show a sensitivity table of terminal growth rate vs discount rate alongside any DCF output, or the number is meaningless as a standalone figure.
Monthly churn compounded annually is much worse than it looks - 2% monthly churn sounds small but means ~21% annual revenue loss. Founders often model monthly churn in isolation and miss the compounding effect on ARR. Build a cohort model that shows the revenue retention curve over 12-24 months to make this visible.
Option pool shuffle dilutes founders pre-money, not investors post-money - When VCs require an option pool refresh at the time of investment, they typically require it to be created using pre-money shares. This means founders bear 100% of the dilution. A $10M pre-money valuation with a 10% option pool refresh effectively reduces the founder's pre-money valuation to ~$9M. Model this explicitly in cap table scenarios.
Blended CAC hides channel efficiency differences - If paid search CAC is $5,000 and organic CAC is $500, a blended $2,000 CAC looks reasonable but the business is critically dependent on a channel that could turn off. Always model CAC by channel separately to understand which channels are economically viable.
"Scenario analysis" with only revenue assumptions changed is not scenario analysis - A true scenario represents a coherent narrative where multiple assumptions change together (growth rate, churn, gross margin, sales efficiency all move in the same direction). Changing only one variable while holding others constant is sensitivity analysis, which is a different and complementary tool.
For detailed benchmarks, formulas, and worked examples:
references/saas-metrics.md - SaaS financial metrics definitions, benchmarks, and
industry standards (MRR, ARR, NRR, LTV:CAC, Rule of 40, magic number)Only load a references file if the current task requires it - they are detailed and will consume context.
On first activation of this skill in a conversation: check which companion skills are installed by running
ls ~/.claude/skills/ ~/.agent/skills/ ~/.agents/skills/ .claude/skills/ .agent/skills/ .agents/skills/ 2>/dev/null. Compare the results against therecommended_skillsfield in this file's frontmatter. For any that are missing, mention them once and offer to install:npx skills add AbsolutelySkilled/AbsolutelySkilled --skill <name>Skip entirely if
recommended_skillsis empty or all companions are already installed.