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Get Started Free →Run financial ratio analysis, DCF valuation, budget variance analysis, and rolling forecasts. Usage: /financial-health <ratios|dcf|budget|forecast> <data.json>
.claude/skills/alirezarezvani-financial-health/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-21 | ✗→✓ | ▲ Improved | -32% | 0% |
| case-22 | ✗→✓ | ▲ Improved | 27% | 0% |
| case-15 | ✗→✓ | ▲ Improved | -3% | 0% |
| case-01 | ✗→✓ | ▲ Improved | 29% | 0% |
| case-04 | ✗→✓ | ▲ Improved | -28% | 0% |
Analyze financial statements, build valuation models, assess budget variances, and construct forecasts.
/financial-health ratios <financial_data.json> [--format json|text]
/financial-health dcf <valuation_data.json> [--format json|text]
/financial-health budget <budget_data.json> [--format json|text]
/financial-health forecast <forecast_data.json> [--format json|text]/financial-health ratios quarterly_financials.json --format json
/financial-health dcf acme_valuation.json
/financial-health budget q1_budget.json --format json
/financial-health forecast revenue_history.jsonfinance/skills/financial-analyst/scripts/ratio_calculator.py — Profitability, liquidity, leverage, efficiency, valuation ratiosfinance/skills/financial-analyst/scripts/dcf_valuation.py — DCF enterprise and equity valuation with sensitivity analysisfinance/skills/financial-analyst/scripts/budget_variance_analyzer.py — Actual vs budget vs prior year variance analysisfinance/skills/financial-analyst/scripts/forecast_builder.py — Driver-based revenue forecasting with scenario modeling→ finance/skills/financial-analyst/SKILL.md
/saas-health — SaaS-specific metrics (ARR, MRR, churn, CAC, LTV, Quick Ratio)| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-21 | fail→pass | 9,346 | 4,136 | -56% | 1 | 1 | 0% | 1,562 | 1,067 | -32% | 0 | 0 | — |
case-22 | fail→pass | 11,152 | 14,471 | +30% | 1 | 1 | 0% | 2,504 | 3,177 | +27% | 0 | 0 | — |
case-15 | fail→pass | 4,308 | 2,227 | -48% | 1 | 1 | 0% | 749 | 730 | -3% | 0 | 0 | — |
case-13 | pass→pass | 4,297 | 2,661 | -38% | 1 | 1 | 0% | 769 | 845 | +10% | 0 | 0 | — |
case-14 | pass→pass | 6,329 | 2,532 | -60% | 1 | 1 | 0% | 1,039 | 808 | -22% | 0 | 0 | — |
case-01 | fail→pass | 7,966 | 8,933 | +12% | 1 | 1 | 0% | 1,505 | 1,939 | +29% | 0 | 0 | — |
case-02 | fail→fail | 12,295 | 9,024 | -27% | 1 | 1 | 0% | 2,611 | 598 | -77% | 0 | 0 | — |
case-03 | fail→fail | 3,052 | 7,635 | +150% | 1 | 1 | 0% | 480 | 750 | +56% | 0 | 0 | — |
case-04 | fail→pass | 22,943 | 19,512 | -15% | 1 | 1 | 0% | 4,616 | 3,319 | -28% | 0 | 0 | — |
case-16 | pass→pass | 9,121 | 1,705 | -81% | 1 | 1 | 0% | 1,366 | 616 | -55% | 0 | 0 | — |
case-05 | fail→fail | 12,085 | 12,712 | +5% | 1 | 1 | 0% | 2,668 | 3,429 | +29% | 0 | 0 | — |
case-06 | fail→fail | 23,713 | 18,301 | -23% | 1 | 1 | 0% | 4,784 | 4,164 | -13% | 0 | 0 | — |
case-07 | fail→fail | 19,623 | 10,947 | -44% | 1 | 1 | 0% | 4,429 | 2,531 | -43% | 0 | 0 | — |
case-08 | fail→fail | 5,408 | 8,591 | +59% | 1 | 1 | 0% | 988 | 936 | -5% | 0 | 0 | — |
case-09 | fail→pass | 9,858 | 1,867 | -81% | 1 | 1 | 0% | 1,805 | 690 | -62% | 0 | 0 | — |
case-10 | fail→pass | 7,148 | 2,336 | -67% | 1 | 1 | 0% | 1,115 | 773 | -31% | 0 | 0 | — |
case-11 | fail→pass | 6,755 | 2,239 | -67% | 1 | 1 | 0% | 1,050 | 713 | -32% | 0 | 0 | — |
case-12 | fail→pass | 12,930 | 2,502 | -81% | 1 | 1 | 0% | 2,567 | 705 | -73% | 0 | 0 | — |
case-17 | pass→pass | 4,891 | 1,785 | -64% | 1 | 1 | 0% | 718 | 594 | -17% | 0 | 0 | — |
case-18 | pass→pass | 7,399 | 1,880 | -75% | 1 | 1 | 0% | 1,139 | 626 | -45% | 0 | 0 | — |
case-19 | fail→pass | 5,699 | 1,754 | -69% | 1 | 1 | 0% | 703 | 519 | -26% | 0 | 0 | — |
case-20 | fail→pass | 9,030 | 3,143 | -65% | 1 | 1 | 0% | 1,545 | 897 | -42% | 0 | 0 | — |
case-23 | fail→pass | 5,885 | 2,360 | -60% | 1 | 1 | 0% | 866 | 696 | -20% | 0 | 0 | — |
case-24 | fail→pass | 10,984 | 2,999 | -73% | 1 | 1 | 0% | 1,763 | 777 | -56% | 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. 24 cases were attempted, and 21 counted toward the lift figure. The other 3 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 +54 percentage points is the difference between those two pass rates over the 21 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.