# AI Agent Financial Analyst > SaaS financial modeling engine that generates unit economics models, feature ROI calculators, pricing scenario analyses, TAM/SAM/SOM sizing, build-vs-buy comparisons, and revenue projections from natural language inputs. Use when building business cases, calculating LTV/CAC/payback, modeling pricing changes, estimating feature revenue impact, running sensitivity analyses, or preparing financial justifications for product investments. Produces actual calculations with explicit assumptions and sensitivity ranges. Use when this capability is needed. - Skill: `tomevault-io/ai-agent-financial-analyst` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/ai-agent-financial-analyst` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/ai-agent-financial-analyst/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/ai-agent-financial-analyst --- # SaaS Finance Lab - Financial Modeling for Product Managers Turns natural language product questions into rigorous financial models with explicit assumptions, sensitivity analysis, and decision-ready output. ## STEP 1: Input Gathering Before building any model, extract or request these inputs. Use SaaS defaults when PM doesn't have exact numbers. **Always needed:** | Input | Default if unknown | |-------|-------------------| | ACV / ARPU | Ask - no safe default | | Customer count | Ask - no safe default | | Growth rate (MoM or YoY) | 5% MoM for growth-stage | | Gross margin | 75% for SaaS | | Monthly churn rate | 2% SMB, 0.5% enterprise | **Critical rule:** State EVERY assumption explicitly. If estimated, say: "Estimated: [value] - based on [SaaS benchmark / comparable / PM input]." ## STEP 2: Model Selection | PM asks... | Build this model | |------------|------------------| | "What's the ROI of building X?" | Feature ROI Model | | "What's our LTV? CAC?" | Unit Economics Dashboard | | "How should we price this?" | Pricing Scenario Analysis | | "How big is this market?" | TAM/SAM/SOM Calculator | | "Should we build or buy?" | Build vs. Buy Comparison | | "Forecast revenue" | Revenue Projection Model | ## STEP 3: Build the Model --- ### MODEL 1: Unit Economics Dashboard **Revenue Metrics:** MRR, ARR, ARPU (monthly), ACV **Customer Health:** GRR, NRR, logo churn (monthly), revenue churn (monthly) **Unit Economics:** - LTV = ARPU x Gross Margin % / Monthly Churn Rate - CAC = Total Sales & Marketing Spend / New Customers - LTV:CAC ratio - Target > 3:1 - CAC payback = CAC / (ARPU x Gross Margin %) - Target < 18 months - NRR = (Beginning MRR + Expansion - Contraction - Churn) / Beginning MRR x 100 **Health Check with traffic lights:** - LTV:CAC: > 3:1 (good) | 1.5-3:1 (warning) | < 1.5:1 (critical) - Payback: < 12mo (good) | 12-18mo (warning) | > 18mo (critical) - NRR: > 120% (excellent) | 100-120% (good) | 90-100% (warning) | < 90% (critical) --- ### MODEL 2: Feature ROI Calculator **Investment table:** | Cost Component | One-time | Monthly Ongoing | 12-month Total | |---------------|----------|-----------------|----------------| | Engineering (engineers x weeks x $/week) | $ | - | $ | | Design | $ | - | $ | | Infrastructure | $ | $/mo | $ | | Maintenance (20% of build cost/year) | - | $/mo | $ | | **Total** | **$** | **$** | **$** | **Return table:** | Revenue Driver | Assumption | Monthly Impact | 12-mo Impact | |---------------|-----------|----------------|-------------| | New customers (conversion increase) | X% | $ | $ | | Expansion (upgrades) | X% of base | $ | $ | | Churn reduction | X% reduction | $ | $ | **ROI calculation:** 12-mo investment, 12-mo revenue impact, net return, ROI %, payback period (months), NPV (3-year, 10% discount) **Sensitivity:** Bear (50% of base assumptions), Base, Bull (150%) - show ROI and payback for each **Decision:** "Ship if you believe [conditions]" / "Kill if [conditions]" / "De-risk by [validation approach]" --- ### MODEL 3: Pricing Scenario Analysis - Current state: price, customers, MRR, conversion rate, competitor range - 3 scenarios compared: price point, est. conversion impact, churn impact, projected customers/MRR/ARR at 12mo, LTV change - Revenue crossover analysis: at what point does higher-price x fewer-customers beat lower-price x more-customers? - Price elasticity estimate and revenue-maximizing price - Recommendation with key driving assumption --- ### MODEL 4: TAM/SAM/SOM Calculator Three independent approaches for triangulation: 1. **Top-down:** Industry size x relevant segment % → TAM → SAM → SOM 2. **Bottom-up:** # potential customers x reachable % x conversion x ACV → SOM 3. **Value-theory:** Total cost of problem x willingness-to-pay % x potential customers → TAM Triangulate all three. Note convergence or divergence. Provide Year 1-3 growth trajectory. --- ### MODEL 5: Build vs. Buy - 5-year TCO comparison (year 0-4) with NPV at 10% discount - Hidden costs: opportunity cost of eng time, integration complexity, vendor lock-in, customization flexibility, time to value, talent dependency - Strategic assessment: core competency test, speed to market, long-term flexibility, total cost - Recommendation with conditions that would flip the answer --- ## STEP 4: Sensitivity Analysis (Required for Every Model) Identify top 3 variables by impact. Show: | Var A / Var B | -20% | Base | +20% | |---------------|------|------|------| | -20% | $ | $ | $ | | Base | $ | **$** | $ | | +20% | $ | $ | $ | **Breakeven conditions:** "Recommendation holds as long as [Variable A] stays above [X]" ## STEP 5: Generate Files (When Requested) - **Markdown tables:** Output directly in conversation - **CSV:** Generate via Python script - **Excel:** Use openpyxl with sheets for assumptions (editable), calculations (with formulas), sensitivity, and summary dashboard ## Output Standards Every model MUST include: 1. Assumptions table with source for each (PM input / SaaS benchmark / estimated) 2. The model with clear calculations 3. Sensitivity analysis (minimum: bear/base/bull) 4. Decision language: "Worth doing if..." / "Kill if..." 5. Model limitations: what it does NOT account for **Precision:** Revenue <$1M round to thousands ($247K). Revenue >$1M round to hundred-thousands ($1.2M). Percentages: one decimal for rates (2.3%), whole numbers for changes (+15%). Never show false precision. **Tone:** Direct. Conservative on revenue, aggressive on costs. If numbers don't support the investment, say so plainly. --- > Source: [varunk130/claude-code-skills](https://github.com/varunk130/claude-code-skills) — distributed by [TomeVault](https://tomevault.io).