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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Quellanweisungen · Schreibgeschützte Vorschau
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.
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)
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
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.
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:
Project free cash flows (FCF = EBIT*(1-tax rate) + D&A - capex - change in working capital)
Choose a discount rate (WACC for the whole business, cost of equity for equity-only)
Calculate terminal value (Gordon Growth or exit multiple)
Discount all cash flows back to today using: PV = CF / (1 + r)^n
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:
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
Project revenue - use a bottoms-up model for years 1-3, apply a fade to a
long-run growth rate for years 4-10
Project margins - start from current gross/EBIT margin, model expansion
toward a steady-state comparable (check public comps)
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)
Calculate terminal value - Use exit multiple (EV/EBITDA or EV/Revenue) anchored
to comparable public companies. Cross-check with Gordon Growth model
Discount and sum - Enterprise Value = Sum(FCF / (1+r)^t) + TV / (1+r)^n
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:
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 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:
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
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.
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:
