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 j…
Install
$ agentstack add skill-varunk130-claude-code-skills-ai-agent-financial-analyst ✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
About
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 3:1 (good) | 1.5-3:1 (warning) | 18mo (critical)
- NRR: > 120% (excellent) | 100-120% (good) | 90-100% (warning) | $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 & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: varunk130
- Source: varunk130/claude-code-skills
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.