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Get Started Free →Router/index for the 2 finance skills bundled in this plugin: financial-analyst (ratio analysis, DCF valuation, budget variance, rolling forecasts) and saas-metrics-coach (ARR/MRR, churn, CAC/LTV, NRR, quick ratio). Use when a finance request doesn't obviously match one skill and you need to pick the right one (e.g., 'analyze these financials', 'how healthy are my SaaS metrics').
.claude/skills/alirezarezvani-finance-skills/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-03 | ✗→✓ | ▲ Improved | -64% | 0% |
| case-05 | ✗→✓ | ▲ Improved | -33% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 16% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 6% | 0% |
| case-08 | ✗→✓ | ▲ Improved | -69% | 0% |
This plugin bundles 2 finance skills (this router is the 3rd folder under finance/skills/). Each skill is self-contained.
| Request signals | Skill | Path | |---|---|---| | Ratio analysis, DCF valuation, budget variance, driver-based forecasts | financial-analyst | skills/financial-analyst/ | | ARR/MRR, churn, CAC/LTV, NRR, quick ratio, SaaS benchmarks | saas-metrics-coach | skills/saas-metrics-coach/ |
If both match (e.g., "value my SaaS company"), ask whether the user wants statement-level analysis (financial-analyst) or SaaS operating metrics (saas-metrics-coach).
bash# Example: route a statement-analysis request cat finance/skills/financial-analyst/SKILL.md python3 finance/skills/financial-analyst/scripts/ratio_calculator.py --help # Or a SaaS metrics request python3 finance/skills/saas-metrics-coach/scripts/metrics_calculator.py --help
finance/business-investment-advisor/ — investment thesis evaluation, ROI modeling (prompt-only skill, separate nested plugin)/financial-health and /saas-health wrap these skills' scripts.| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-03 | fail→pass | 13,344 | 2,676 | -80% | 1 | 1 | 0% | 2,302 | 840 | -64% | 0 | 0 | — |
case-01 | fail→fail | 8,530 | 4,286 | -50% | 1 | 1 | 0% | 1,511 | 1,108 | -27% | 0 | 0 | — |
case-02 | fail→fail | 8,711 | 5,622 | -35% | 1 | 1 | 0% | 1,707 | 1,340 | -21% | 0 | 0 | — |
case-04 | fail→fail | 6,215 | 5,222 | -16% | 1 | 1 | 0% | 1,165 | 1,403 | +20% | 0 | 0 | — |
case-05 | fail→pass | 14,505 | 8,189 | -44% | 1 | 1 | 0% | 2,713 | 1,810 | -33% | 0 | 0 | — |
case-06 | fail→pass | 5,896 | 4,379 | -26% | 1 | 1 | 0% | 1,013 | 1,179 | +16% | 0 | 0 | — |
case-07 | fail→pass | 6,855 | 4,833 | -29% | 1 | 1 | 0% | 1,195 | 1,261 | +6% | 0 | 0 | — |
case-08 | fail→pass | 15,835 | 3,210 | -80% | 1 | 1 | 0% | 2,923 | 901 | -69% | 0 | 0 | — |
case-09 | fail→pass | 10,878 | 3,711 | -66% | 1 | 1 | 0% | 1,876 | 944 | -50% | 0 | 0 | — |
case-10 | pass→pass | 8,494 | 5,805 | -32% | 1 | 1 | 0% | 1,510 | 1,358 | -10% | 0 | 0 | — |
case-11 | fail→fail | 23,193 | 14,262 | -39% | 1 | 1 | 0% | 3,879 | 2,657 | -32% | 0 | 0 | — |
case-12 | fail→fail | 17,955 | 12,245 | -32% | 1 | 1 | 0% | 3,829 | 2,886 | -25% | 0 | 0 | — |
case-13 | fail→pass | 9,075 | 4,232 | -53% | 1 | 1 | 0% | 1,723 | 1,043 | -39% | 0 | 0 | — |
case-14 | fail→pass | 4,502 | 1,267 | -72% | 1 | 1 | 0% | 758 | 588 | -22% | 0 | 0 | — |
case-15 | fail→pass | 3,407 | 1,681 | -51% | 1 | 1 | 0% | 561 | 603 | +7% | 0 | 0 | — |
case-16 | pass→pass | 13,991 | 9,029 | -35% | 1 | 1 | 0% | 2,362 | 1,996 | -15% | 0 | 0 | — |
case-17 | fail→fail | 5,158 | 4,680 | -9% | 1 | 1 | 0% | 966 | 1,179 | +22% | 0 | 0 | — |
case-18 | pass→pass | 11,259 | 2,711 | -76% | 1 | 1 | 0% | 1,854 | 846 | -54% | 0 | 0 | — |
case-19 | fail→pass | 6,830 | 3,192 | -53% | 1 | 1 | 0% | 1,200 | 938 | -22% | 0 | 0 | — |
case-20 | pass→pass | 7,759 | 1,230 | -84% | 1 | 1 | 0% | 1,363 | 558 | -59% | 0 | 0 | — |
case-21 | fail→fail | 16,140 | 6,454 | -60% | 1 | 1 | 0% | 4,027 | 1,619 | -60% | 0 | 0 | — |
case-22 | fail→pass | 7,640 | 1,739 | -77% | 1 | 1 | 0% | 1,353 | 678 | -50% | 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 +50 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.