seed-model

The Financial Modeler turns a handful of assumptions into the 36 months an investor will ask about. You say what a walk (or an order, or a seat) costs, how much of it you keep, how many customers you win and lose, who you hire and when, and how much you are raising. It does the arithmetic, every month, to the cent, and tells you when the money runs out, when the business pays for itself and what each customer is worth. Every number traces back to an assumption you can change and defend. It is a plan, not a forecast: the model is only as honest as its inputs.

${CLAUDE_SKILL_DIR} below is this skill's folder (where this SKILL.md lives).

python3 "${CLAUDE_SKILL_DIR}/model.py" init                # seed/assumptions.json from the example
python3 "${CLAUDE_SKILL_DIR}/model.py" run                 # the summary, nothing written
python3 "${CLAUDE_SKILL_DIR}/model.py" run --write         # also writes seed/model.json
python3 "${CLAUDE_SKILL_DIR}/model.py" run --months        # every month in a table
python3 "${CLAUDE_SKILL_DIR}/model.py" funds               # the use of funds against the raise
python3 "${CLAUDE_SKILL_DIR}/model.py" csv                 # seed/model.csv for a spreadsheet

Other flags: --dir PATH (default ./seed), --assumptions PATH, --json, csv --out - to print the CSV.

What it reads and writes

file	what
assumptions.example.json (this folder)	the Leashly example init copies: every field filled in
seed/assumptions.json	your assumptions: price, take rate, cost per order, customers, CAC, churn, payroll, opex, one-off costs, the raise, the use of funds
seed/model.json	with run --write: the assumptions, 36 monthly rows, the summary and the unit economics. seed-plan, seed-deck and seed-coach read it
seed/model.csv	with csv: the 36 rows, one column per line item

The assumptions

field	what it means
price	what a customer pays for one unit (a $25 walk)
take_rate_pct	the share of that price you keep (15 means 15%)
cost_per_order	what one unit costs you: pct_of_price and/or fixed, like card fees (2.9% + $0.30)
customers	starting, new_month_1, new_growth_pct (one monthly rate a year), churn_monthly_pct, orders_per_customer_month
cac	what it costs to win one new customer; marketing = new customers x CAC
payroll, payroll_overhead_pct	each role's monthly pay and start month; overhead covers payroll taxes and benefits
opex, one_off	monthly costs (software, insurance) and one-time costs in a given month (a city launch)
raise, raise_month, cash_at_start	the round, the month it lands, cash before it
use_of_funds	{what: amount}, adding up to the raise

The method

Each month: customers = last month's minus churn plus the new ones. Orders = customers x orders a month. GMV = orders x price. Revenue = GMV x take rate. Gross profit (the contribution) = revenue minus the per-order costs. Opex = payroll with overhead + marketing + monthly opex + any one-off cost. Net = gross profit minus opex. Cash = last month's cash + net (+ the raise when it lands). Customers and orders round half up to whole numbers; money is exact to the cent.

• Breakeven month: the first month with net at or above $0 that every later month keeps. A single good month does not count.
• Peak burn: the biggest monthly loss, and the month it happens.
• Runway: the months the cash covers before it first goes below $0. If it never does in 36 months, the summary says the cash lasts all 36.
• Unit economics: revenue per order = price x take rate. Contribution per order = that minus the costs of one order. CAC payback = CAC / (contribution per order x orders a month). LTV = monthly contribution per customer / monthly churn. LTV:CAC = LTV / CAC.
• Use of funds: must add up to the raise to the cent, or funds exits 1 and says by how much it is short or over.

Steps

1. If seed/assumptions.json does not exist, run model.py init and walk the founder through every field in plain words. Replace the Leashly numbers; never keep an example number the founder did not confirm.
2. Ask where each number comes from (a pilot, a quote, a competitor's public price). Note the source next to it in the chat. Guesses are fine if they are called guesses.
3. Run model.py run. Read back the yearly table, the unit economics, breakeven, peak burn and runway.
4. If the cash runs out before breakeven, say so plainly and show the two levers: raise more, or cut the plan (slower hiring, lower CAC). Never tweak an assumption on your own to make the picture prettier.
5. Run model.py funds until the use of funds adds up to the raise.
6. Run model.py run --write, then model.py csv if they want a spreadsheet.
7. Hand off to /seed-plan (it pulls these numbers into the business plan), then /seed-deck. /seed-coach will ask about every number here.

Example: the reel

Leashly (fictional): $25 flat walks, a 15% take rate, card processing 2.9% + $0.30 and a $0.50 backup-walker standby fee per walk, 210 pilot owners, 150 new owners in month 1 growing 10% a month in year 1, 7% monthly churn, 8 walks per owner a month, a $60 CAC, a $1.5M seed landing in month 1 (Jan 2027). model.py run:

Leashly: 36-month model, Jan 2027 to Dec 2029 (USD)
                             Year 1         Year 2         Year 3
  Walks                     124,640        423,104        798,272
  GMV                    $3,116,000    $10,577,600    $19,956,800
  Revenue                  $467,400     $1,586,640     $2,993,520
  Gross profit             $277,324       $941,406     $1,776,155
  Opex                     $957,800     $1,467,120     $1,679,880
  Net                     -$680,476      -$525,714        $96,275
  Cash at year end         $819,524       $293,810       $390,086
  Customers (end)             2,490          6,200          9,999

  Per walk           price $25.00, you keep $3.75 (15%), costs $1.53, contribution $2.23
  Per customer       LTV $254.29 on a $60.00 CAC: LTV:CAC 4.24x, CAC paid back in 3.37 months
  Breakeven          month 29 (May 2029), and every month after it
  Peak burn          $82,801 in month 5 (May 2027)
  Runway             the cash lasts all 36 months (low point $252,553 in month 28)
  Use of funds       adds up to the $1,500,000 raise

Peak burn lands in month 5 because the second city's $25,000 launch cost hits that month. The use of funds: product and engineering $600,000 (40%), city launches $450,000 (30%), marketing $300,000 (20%), operations and insurance $150,000 (10%).

Rules

• Every number comes from seed/assumptions.json. Nothing is looked up or invented by the tool.
• Projections from your own assumptions, not a forecast and not a promise of funding. Not investment advice.
• Show the bad months as clearly as the good ones: burn, the low point, a runway that ends.
• Never change an assumption to hit a target without saying so and getting the founder's yes.