When this skill is activated, always start your first response with the 🧢 emoji.

Financial Modeling

A practitioner's framework for building financial models that inform real decisions. This skill covers the mechanics of DCF valuation, revenue forecasting, unit economics, scenario analysis, and cap tables - with emphasis on what drives the numbers, not just how to calculate them. Designed for founders, operators, and analysts who need models that hold up to scrutiny.

---

When to use this skill

Trigger this skill when the user:

• Builds a revenue forecast or bottoms-up SaaS model
• Performs a DCF valuation or wants to value a business
• Models unit economics (LTV, CAC, payback period, contribution margin)
• Creates scenario analysis (base, bull, bear cases)
• Builds or updates a cap table (pre/post-money, option pool, dilution)
• Models operating expenses by department or headcount plan
• Runs sensitivity analysis or builds data tables
• Prepares financial projections for a board, investor, or fundraise

Do NOT trigger this skill for:

• Accounting or tax compliance questions (use a CPA, not a model)
• Real-time market data, stock screening, or trading strategies

---

Key principles

1. 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.

2. 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.

3. 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.

4. 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.

5. 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.

---

Core concepts

Three-statement model

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.

DCF mechanics

A DCF (Discounted Cash Flow) values a business by the present value of its future free cash flows. The mechanics:

1. Project free cash flows (FCF = EBIT*(1-tax rate) + D&A - capex - change in working capital)
2. Choose a discount rate (WACC for the whole business, cost of equity for equity-only)
3. Calculate terminal value (Gordon Growth or exit multiple)
4. Discount all cash flows back to today using: PV = CF / (1 + r)^n
5. Sum the present values - that is the enterprise value

The 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

Unit economics measure the profitability of a single customer or transaction:

• LTV (Lifetime Value): (ARPU * Gross Margin %) / Churn Rate
• CAC (Customer Acquisition Cost): Total sales & marketing spend / new customers acquired
• LTV:CAC ratio: Benchmark 3:1 or higher for healthy SaaS
• CAC Payback Period: CAC / (ARPU * Gross Margin %) - months to recover acquisition cost
• Contribution Margin: Revenue minus variable costs per unit

Cap table structure

A cap table tracks ownership in a company across all shareholders:

• Pre-money valuation: Company value before new investment
• Post-money valuation: Pre-money + new investment
• Price per share: Pre-money valuation / fully diluted shares outstanding
• Dilution: Each new share issued reduces existing shareholders' ownership percentage
• Option pool shuffle: Investors often require the option pool to be created pre-money, which dilutes founders, not investors - model this explicitly

---

Common tasks

Build a SaaS revenue forecast - bottoms-up model

Start 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.

Build a DCF valuation - step by step

1. Project revenue - use a bottoms-up model for years 1-3, apply a fade to a long-run growth rate for years 4-10
2. Project margins - start from current gross/EBIT margin, model expansion toward a steady-state comparable (check public comps)
3. Calculate unlevered FCF - EBIT * (1-tax) + D&A - Capex - change in NWC
4. Set the discount rate - For early-stage: use 20-35% (reflects risk premium). For public comps-based: use WACC (8-12% range for established businesses)
5. Calculate terminal value - Use exit multiple (EV/EBITDA or EV/Revenue) anchored to comparable public companies. Cross-check with Gordon Growth model
6. Discount and sum - Enterprise Value = Sum(FCF / (1+r)^t) + TV / (1+r)^n
7. Bridge to equity value - Equity Value = Enterprise Value - Net Debt

Sanity 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.

Model unit economics - LTV/CAC/payback

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.

Create scenario analysis - base/bull/bear

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.

Build a cap table - pre/post money

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)

Model operating expenses - by department

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.

Sensitivity analysis - data tables

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-patterns

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

---

Gotchas

1. 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.

2. 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.

3. 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.

4. 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.

5. "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.

---

References

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.

---

Companion check

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 the recommended_skills field 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_skills is empty or all companions are already installed.