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Get Started Free →Cash runway as a distribution, not a number — Monte Carlo simulated. Use when someone asks how long their cash lasts, when to start fundraising, or how burn/revenue volatility changes their runway; especially when the naive cash÷burn answer is driving a decision. Produces P10/P50/P90 runway, month-by-month death probabilities, and a real .xlsx with editable assumptions and a live naive-runway formula — via the bundled zero-dependency simulator.
.claude/skills/mohitagw15856-runway-monte-carlo/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | 59% | 0% |
| case-07 | ✗→✓ | ▲ Improved | -19% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 39% | 0% |
| case-14 | ✗→✓ | ▲ Improved | -12% | 0% |
| case-15 | ✗→✓ | ▲ Improved | 22% | 0% |
"Cash divided by burn" is one path through a fan of thousands. Real burn wobbles, revenue growth compounds or doesn't, and the difference between the median path and the unlucky-decile path is the difference between a calm raise and a bridge round. This skill runs the simulation — thousands of paths, actual random draws by the bundled script — and reports runway the way it actually behaves: as percentiles.
This skill ships scripts/runway_sim.py — zero dependencies, deterministic with --seed:
bashpython3 scripts/runway_sim.py run runway.xlsx --cash 2400000 --burn 210000 --burn-vol 0.12 \ --revenue 60000 --rev-growth 0.05 --rev-vol 0.3
It prints the percentiles (naive=16.0mo P10=19 P50=>36 P90=>36 survive(36mo)=56.8%) and writes an .xlsx with an Assumptions sheet (editable cash/burn/revenue cells, live naive-runway formula) and a Death curve sheet. Requires a code-execution environment.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 39,632 | 16,212 | -59% | 1 | 1 | 0% | 5,472 | 1,251 | -77% | 0 | 0 | — |
case-02 | fail→fail | 33,590 | 19,332 | -42% | 1 | 1 | 0% | 5,372 | 2,286 | -57% | 0 | 0 | — |
case-03 | fail→fail | 42,336 | 19,395 | -54% | 1 | 1 | 0% | 5,631 | 1,361 | -76% | 0 | 0 | — |
case-04 | fail→pass | 20,479 | 23,338 | +14% | 1 | 1 | 0% | 2,428 | 3,865 | +59% | 0 | 0 | — |
case-05 | fail→fail | 18,587 | 17,510 | -6% | 1 | 1 | 0% | 1,919 | 1,732 | -10% | 0 | 0 | — |
case-06 | pass→pass | 18,462 | 2,292 | -88% | 1 | 1 | 0% | 2,376 | 1,180 | -50% | 0 | 0 | — |
case-07 | fail→pass | 10,573 | 8,104 | -23% | 1 | 1 | 0% | 1,477 | 1,196 | -19% | 0 | 0 | — |
case-08 | pass→pass | 17,203 | 16,943 | -2% | 1 | 1 | 0% | 1,827 | 2,523 | +38% | 0 | 0 | — |
case-09 | fail→fail | 11,010 | 7,175 | -35% | 1 | 1 | 0% | 1,670 | 1,150 | -31% | 0 | 0 | — |
case-10 | pass→pass | 21,132 | 16,487 | -22% | 1 | 1 | 0% | 2,447 | 2,735 | +12% | 0 | 0 | — |
case-11 | fail→pass | 23,117 | 24,670 | +7% | 1 | 1 | 0% | 2,706 | 3,752 | +39% | 0 | 0 | — |
case-12 | pass→pass | 21,788 | 25,329 | +16% | 1 | 1 | 0% | 2,328 | 4,240 | +82% | 0 | 0 | — |
case-13 | pass→pass | 12,615 | 25,847 | +105% | 1 | 1 | 0% | 2,154 | 3,184 | +48% | 0 | 0 | — |
case-14 | fail→pass | 23,310 | 7,370 | -68% | 1 | 1 | 0% | 1,231 | 1,087 | -12% | 0 | 0 | — |
case-15 | fail→pass | 10,798 | 6,766 | -37% | 1 | 1 | 0% | 874 | 1,067 | +22% | 0 | 0 | — |
case-16 | pass→pass | 15,487 | 7,893 | -49% | 1 | 1 | 0% | 1,553 | 2,100 | +35% | 0 | 0 | — |
case-17 | pass→fail | 15,528 | 22,763 | +47% | 1 | 1 | 0% | 2,256 | 3,198 | +42% | 0 | 0 | — |
case-18 | pass→pass | 17,462 | 8,120 | -53% | 1 | 1 | 0% | 1,657 | 1,419 | -14% | 0 | 0 | — |
case-19 | pass→pass | 14,750 | 12,886 | -13% | 1 | 1 | 0% | 2,204 | 1,898 | -14% | 0 | 0 | — |
case-20 | pass→pass | 22,220 | 52,700 | +137% | 1 | 1 | 0% | 3,411 | 9,064 | +166% | 0 | 0 | — |
case-21 | pass→pass | 19,615 | 20,813 | +6% | 1 | 1 | 0% | 3,322 | 4,111 | +24% | 0 | 0 | — |
case-22 | pass→pass | 17,560 | 21,459 | +22% | 1 | 1 | 0% | 1,784 | 3,299 | +85% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 17 counted toward the lift figure. The other 5 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +18 percentage points is the difference between those two pass rates over the 17 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.