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Get Started Free →AI CFO for bootstrapped startups. Provides financial frameworks for cash management, runway calculations, unit economics (LTV:CAC), capital allocation, hiring ROI, burn rate analysis, working capital optimization, and forecasting.
.claude/skills/cowork-os-startup-cfo/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-20 | ✗→✓ | ▲ Improved | -34% | 0% |
| case-01 | ✓→✓ | = Same ✓ | -12% | 0% |
| case-02 | ✓→✓ | = Same ✓ | 55% | 0% |
| case-03 | ✓→✓ | = Same ✓ | 15% | 0% |
| case-04 | ✓→✓ | = Same ✓ | 10% | 0% |
AI CFO for bootstrapped startups. Provides financial frameworks for cash management, runway calculations, unit economics (LTV:CAC), capital allocation, hiring ROI, burn rate analysis, working capital optimization, and forecasting.
| Name | Type | Required | Description | |---|---|---|---| | topic | select | Yes | Area of focus | | question | string | Yes | Your specific question | | arr | string | No | Annual Recurring Revenue (e.g., $2M) | | monthlyChurn | string | No | Monthly churn rate (e.g., 2.5%) | | cac | string | No | Customer Acquisition Cost (e.g., $500) | | ltv | string | No | Lifetime Value (e.g., $3000) |
../startup-cfo.json.| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | pass→pass | 12,147 | 7,912 | -35% | 1 | 1 | 0% | 2,109 | 1,864 | -12% | 0 | 0 | — |
case-02 | pass→pass | 6,308 | 8,034 | +27% | 1 | 1 | 0% | 1,145 | 1,775 | +55% | 0 | 0 | — |
case-03 | pass→pass | 10,445 | 8,497 | -19% | 1 | 1 | 0% | 1,795 | 2,056 | +15% | 0 | 0 | — |
case-04 | pass→pass | 10,773 | 9,740 | -10% | 1 | 1 | 0% | 2,023 | 2,219 | +10% | 0 | 0 | — |
case-10 | fail→fail | 14,971 | 14,537 | -3% | 1 | 1 | 0% | 2,883 | 3,319 | +15% | 0 | 0 | — |
case-05 | pass→pass | 6,965 | 8,470 | +22% | 1 | 1 | 0% | 1,405 | 2,030 | +44% | 0 | 0 | — |
case-06 | pass→pass | 11,510 | 9,643 | -16% | 1 | 1 | 0% | 1,946 | 2,027 | +4% | 0 | 0 | — |
case-07 | pass→pass | 12,223 | 8,480 | -31% | 1 | 1 | 0% | 2,081 | 1,891 | -9% | 0 | 0 | — |
case-08 | pass→pass | 4,553 | 6,405 | +41% | 1 | 1 | 0% | 931 | 1,759 | +89% | 0 | 0 | — |
case-09 | pass→pass | 4,195 | 4,738 | +13% | 1 | 1 | 0% | 779 | 1,264 | +62% | 0 | 0 | — |
case-11 | pass→pass | 8,291 | 7,709 | -7% | 1 | 1 | 0% | 1,465 | 1,809 | +23% | 0 | 0 | — |
case-12 | pass→pass | 12,089 | 10,826 | -10% | 1 | 1 | 0% | 1,965 | 2,296 | +17% | 0 | 0 | — |
case-13 | pass→pass | 12,862 | 9,161 | -29% | 1 | 1 | 0% | 2,201 | 1,987 | -10% | 0 | 0 | — |
case-14 | pass→pass | 6,663 | 7,236 | +9% | 1 | 1 | 0% | 1,345 | 1,855 | +38% | 0 | 0 | — |
case-15 | pass→pass | 6,977 | 8,205 | +18% | 1 | 1 | 0% | 1,315 | 1,921 | +46% | 0 | 0 | — |
case-16 | pass→pass | 12,499 | 15,260 | +22% | 1 | 1 | 0% | 2,345 | 3,145 | +34% | 0 | 0 | — |
case-17 | pass→pass | 5,746 | 9,058 | +58% | 1 | 1 | 0% | 1,293 | 2,247 | +74% | 0 | 0 | — |
case-18 | pass→pass | 14,339 | 15,597 | +9% | 1 | 1 | 0% | 2,067 | 2,962 | +43% | 0 | 0 | — |
case-19 | pass→pass | 15,675 | 9,256 | -41% | 1 | 1 | 0% | 2,744 | 2,013 | -27% | 0 | 0 | — |
case-20 | fail→pass | 18,383 | 9,070 | -51% | 1 | 1 | 0% | 2,861 | 1,896 | -34% | 0 | 0 | — |
case-21 | fail→fail | 19,861 | 16,878 | -15% | 1 | 1 | 0% | 3,354 | 3,172 | -5% | 0 | 0 | — |
case-22 | fail→fail | 21,567 | 9,341 | -57% | 1 | 1 | 0% | 2,773 | 1,716 | -38% | 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. The headline lift of +5 percentage points is the difference between those two pass rates over the 22 comparable cases.
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.