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Get Started Free →Financial leadership advisor on financial planning, fundraising, investor reporting, and unit economics. Use when building a financial model, preparing for fundraising, calculating unit economics, or managing cash runway.
.claude/skills/borghei-cfo-advisor/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 116% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 77% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 100% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 177% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 171% | 0% |
The agent acts as a fractional CFO, providing financial strategy and operational finance guidance grounded in SaaS benchmarks, GAAP standards, and investor expectations.
Before building the model or analysis, confirm these inputs. If any is unknown or vague, ASK — do not assume:
Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.
CAC = (Sales + Marketing Spend) / New Customers
CAC Payback = CAC / (ARPU x Gross Margin)
LTV = ARPU x Gross Margin x Customer Lifetime
LTV:CAC Ratio = LTV / CAC Target: > 3:1
Logo Retention = (Customers End - New) / Customers Start
Net Revenue Retention = (MRR End - Churn + Expansion) / MRR StartBurn Multiple = Net Burn / Net New ARR
< 1.0x Excellent efficiency
1.0-1.5x Good efficiency
1.5-2.0x Average
> 2.0x Needs improvementRule of 40 = Revenue Growth % + Profit Margin %
> 40% Strong performance
20-40% Acceptable
< 20% Needs attentionFINANCIAL HIGHLIGHTS
- Revenue: $X.XM (vs Plan: +/-Y%)
- Gross Margin: XX% (vs Plan: +/-Y%)
- Operating Loss: $X.XM (vs Plan: +/-Y%)
- Cash Balance: $X.XM
- Runway: XX months
REVENUE METRICS
- ARR: $X.XM (+Y% QoQ)
- Net New ARR: $XXK
- NRR: XXX%
- Logo Churn: X.X%
EFFICIENCY METRICS
- CAC: $X,XXX
- CAC Payback: XX months
- Burn Multiple: X.Xx| Category | Line Items | |----------|-----------| | Revenue | New business (by segment), expansion, renewals, professional services | | Cost of Revenue | Hosting/infrastructure, support, PS delivery, payment processing | | OpEx | Sales & Marketing, R&D, G&A |
| Days | Activity | |------|----------| | 1-3 | Transaction cutoff | | 3-5 | Reconciliations | | 5-7 | Accruals and adjustments | | 7-10 | Management review | | 10-12 | Final close |
Quality Checklist: Bank reconciliation, revenue recognition, expense accruals, prepaid amortization, deferred revenue, intercompany elimination, flux analysis.
SaaS considerations: Subscription vs usage revenue, implementation services, professional services, multi-year contracts, discounts and credits.
13-Week Cash Flow: Week-by-week projections of all known inflows/outflows. Review weekly. Maintain minimum cash buffer.
Monthly Rolling Forecast: 12-month forward view covering revenue collection timing, payroll, vendor payments, debt service, and CapEx.
Treasury Principles: Maintain 6+ months runway, preserve capital, optimize yield on idle cash, follow investment policy.
Cash Preservation Levers (when extending runway):
Financial data:
Projections:
| Risk Type | Key Concerns | |-----------|-------------| | Market | Interest rate exposure, FX exposure, customer concentration | | Credit | Customer creditworthiness, AR aging, bad debt reserves | | Operational | Internal controls, fraud prevention, systems reliability |
A Series-A company ($3M ARR, 35 employees, $12M raised) preparing for Series B:
Unit Economics:
CAC: $22K | LTV: $88K | LTV:CAC: 4.0x | CAC Payback: 16 months
NRR: 115% | Logo Retention: 90% | Gross Margin: 78%
Burn:
Monthly burn: $350K | Net new ARR/month: $180K
Burn Multiple: 1.9x (average -- needs improvement for Series B)
Cash: $5.2M | Runway: 15 months
Rule of 40:
Revenue growth: 95% YoY | Profit margin: -40%
Score: 55% (strong)
Board recommendation: Raise in 6 months at current trajectory.
Target metrics for raise: Burn Multiple < 1.5x, NRR > 120%.D&O, E&O, Cyber liability, General liability, Workers compensation, Key person insurance.
bash# Unit economics calculator python scripts/unit_economics.py --metrics data.csv # Cash flow projector python scripts/cash_forecast.py --actuals Q1.csv --assumptions model.yaml # Financial model builder python scripts/fin_model.py --template saas --output model.xlsx # Investor metrics dashboard python scripts/investor_metrics.py --period monthly
references/financial_modeling.md -- Model building guidereferences/saas_metrics.md -- SaaS metrics deep divereferences/accounting_policies.md -- Policy documentationreferences/audit_prep.md -- Audit readiness guideComprehensive SaaS financial health assessment: Rule of 40, burn multiple, LTV:CAC, CAC payback, NRR, magic number, and composite score with investor-readiness verdict.
bash# Run with demo data (Series A SaaS) python scripts/financial_health_scorer.py # Quick assessment with key metrics python scripts/financial_health_scorer.py --arr 3000000 --revenue-growth 95 --profit-margin -40 --burn 350000 --cash 5200000 --nrr 115 --gross-margin 78 --headcount 35 # From JSON file python scripts/financial_health_scorer.py --input financials.json # JSON output python scripts/financial_health_scorer.py --input financials.json --json
Models burn rate, runway under 5 scenarios (current, hiring freeze, 10% cut, 20% cut, revenue acceleration), generates 13-week cash flow forecast, and identifies action triggers.
bash# Run with demo data python scripts/burn_rate_calculator.py # Quick calculation python scripts/burn_rate_calculator.py --cash 5200000 --revenue 250000 --expenses 600000 --headcount 35 # JSON output python scripts/burn_rate_calculator.py --json
Three-scenario financial projection engine with probability weighting, sensitivity analysis, and decision triggers. Projects base, upside, and downside cases over 8 quarters.
bash# Run with demo data python scripts/scenario_modeler.py # Quick model from key inputs python scripts/scenario_modeler.py --arr 3000000 --expenses 900000 --cash 5200000 --quarters 8 # From JSON with custom scenarios python scripts/scenario_modeler.py --input scenarios.json # JSON output python scripts/scenario_modeler.py --json
| Problem | Likely Cause | Fix | |---------|-------------|-----| | Burn multiple shows > 3.0x | Spending significantly outpaces net new ARR | Audit S&M efficiency; consider hiring freeze; validate pipeline conversion rates | | Rule of 40 score below 20% | Growth has slowed without corresponding margin improvement | Either re-accelerate growth or cut costs to improve margins -- cannot stay in the middle | | CAC payback exceeds 24 months | Sales cycle too long, ACV too low, or S&M spend too high | Segment CAC by channel; cut underperforming channels; raise ACV through pricing | | LTV:CAC ratio below 2.0x | Customer lifetime too short (churn) or acquisition too expensive | Address churn first (higher ROI); then optimize CAC by channel | | NRR below 100% | Contraction and churn exceed expansion revenue | Build expansion playbook; segment churning customers; invest in customer success | | Financial model assumptions questioned by board | Assumptions not documented or unrealistic | Document every assumption explicitly; show sensitivity analysis for key variables | | Month-end close takes 15+ days | Manual processes, missing reconciliations, or unclear ownership | Implement the Day 1-12 close timeline; assign owners to each checklist item |
In Scope: SaaS unit economics, burn rate analysis, financial modeling, cash management, investor reporting, month-end close, revenue recognition (ASC 606), due diligence preparation, scenario modeling.
Out of Scope: Tax planning, legal entity structuring, audit execution, payroll processing, accounts payable/receivable operations, insurance procurement, equity cap table management.
Limitations: Financial health scorer uses industry benchmarks that may not apply to non-SaaS business models. Burn rate calculator uses linear/exponential approximations -- actual cash flows vary with billing cycles and payment timing. Scenario modeler provides directional guidance, not auditable financial projections.
| Skill | Integration | |-------|-------------| | ceo-advisor | Financial scenarios feed board strategy discussions | | board-deck-builder | Financial update section; all deck numbers validated through CFO tools | | cro-advisor | Revenue forecasting; pipeline-to-revenue conversion assumptions | | chro-advisor | Headcount budget modeling; fully-loaded cost calculations | | ciso-advisor | Compliance budget sizing against quantified risk exposure | | company-os | Financial metrics in the weekly scorecard | | chief-of-staff | Routes financial questions; synthesizes CFO + CEO perspectives |
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 18,934 | 20,076 | +6% | 1 | 1 | 0% | 3,003 | 6,488 | +116% | 0 | 0 | — |
case-02 | fail→fail | 27,691 | 31,137 | +12% | 1 | 1 | 0% | 4,321 | 8,656 | +100% | 0 | 0 | — |
case-03 | fail→pass | 25,908 | 24,895 | -4% | 1 | 1 | 0% | 3,983 | 7,036 | +77% | 0 | 0 | — |
case-04 | fail→pass | 17,832 | 15,521 | -13% | 1 | 1 | 0% | 2,944 | 5,889 | +100% | 0 | 0 | — |
case-19 | pass→pass | 11,262 | 6,504 | -42% | 1 | 1 | 0% | 2,134 | 4,488 | +110% | 0 | 0 | — |
case-05 | fail→pass | 10,821 | 9,658 | -11% | 1 | 1 | 0% | 1,773 | 4,903 | +177% | 0 | 0 | — |
case-06 | fail→pass | 11,512 | 9,975 | -13% | 1 | 1 | 0% | 1,745 | 4,733 | +171% | 0 | 0 | — |
case-07 | pass→pass | 5,678 | 10,335 | +82% | 1 | 1 | 0% | 1,061 | 4,887 | +361% | 0 | 0 | — |
case-08 | pass→pass | 15,031 | 16,966 | +13% | 1 | 1 | 0% | 2,357 | 5,845 | +148% | 0 | 0 | — |
case-09 | fail→pass | 13,054 | 11,682 | -11% | 1 | 1 | 0% | 1,856 | 4,903 | +164% | 0 | 0 | — |
case-10 | pass→pass | 16,816 | 20,054 | +19% | 1 | 1 | 0% | 2,660 | 6,398 | +141% | 0 | 0 | — |
case-11 | pass→pass | 19,074 | 21,251 | +11% | 1 | 1 | 0% | 3,060 | 6,831 | +123% | 0 | 0 | — |
case-12 | fail→pass | 17,944 | 16,797 | -6% | 1 | 1 | 0% | 2,612 | 5,739 | +120% | 0 | 0 | — |
case-13 | pass→pass | 11,983 | 4,898 | -59% | 1 | 1 | 0% | 1,768 | 3,941 | +123% | 0 | 0 | — |
case-14 | pass→pass | 14,780 | 14,777 | -0% | 1 | 1 | 0% | 2,140 | 5,687 | +166% | 0 | 0 | — |
case-15 | fail→pass | 15,630 | 4,603 | -71% | 1 | 1 | 0% | 2,475 | 4,128 | +67% | 0 | 0 | — |
case-16 | fail→pass | 7,194 | 2,414 | -66% | 1 | 1 | 0% | 1,429 | 3,736 | +161% | 0 | 0 | — |
case-17 | fail→pass | 7,455 | 2,211 | -70% | 1 | 1 | 0% | 1,576 | 3,697 | +135% | 0 | 0 | — |
case-18 | pass→pass | 13,260 | 11,446 | -14% | 1 | 1 | 0% | 2,403 | 5,136 | +114% | 0 | 0 | — |
case-20 | fail→fail | 11,542 | 12,301 | +7% | 1 | 1 | 0% | 2,253 | 5,099 | +126% | 0 | 0 | — |
case-21 | pass→pass | 7,442 | 8,203 | +10% | 1 | 1 | 0% | 1,400 | 4,601 | +229% | 0 | 0 | — |
case-22 | fail→pass | 5,090 | 10,053 | +98% | 1 | 1 | 0% | 876 | 4,872 | +456% | 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.