ROI Calculator
CautionBuild ROI calculation models for software projects, marketing campaigns, and infrastructure investments. Produces sensitivity analysis, payback period, NPV, and IRR with interactive spreadsheet outputs.
Install
Claude Code
Copy the SKILL.md file to .claude/skills/roi-calculator.md About This Skill
ROI Calculator builds financial models that give decision-makers the numbers they need to justify investments — with sensitivity analysis to understand which assumptions matter most.
Core Metrics
Calculates: simple ROI percentage, payback period (months to breakeven), Net Present Value (NPV) with a configurable discount rate, and Internal Rate of Return (IRR). Each metric is explained in plain English alongside the formula used.
Cost Modeling
Structures costs into one-time (setup, migration, licensing), recurring (subscriptions, maintenance, headcount), and opportunity costs. Separates capital expenditure from operating expenditure for accounting purposes.
Benefit Quantification
Helps quantify soft benefits: time savings (hours per week × loaded hourly rate × headcount), error reduction (incident rate × cost per incident × reduction percentage), and capacity gains (additional throughput × revenue per unit).
Sensitivity Analysis
Generates a data table showing how NPV changes across a range of key assumptions (adoption rate, cost overrun %, revenue growth). Identifies which variables have the highest impact on the outcome — the assumptions worth scrutinizing.
Output Formats
Produces a Python script with pandas calculations, a CSV for import into Google Sheets or Excel, and a Markdown executive summary with the headline numbers.
Use Cases
- Calculating ROI and payback period for a cloud migration project
- Building an NPV model to justify a new software tool purchase
- Producing a marketing campaign ROI model with cost-per-acquisition breakdown
- Creating a sensitivity analysis for a headcount investment decision
Pros & Cons
Pros
- + Calculates NPV, IRR, payback period, and simple ROI in one model
- + Generates sensitivity analysis to identify high-impact assumptions
- + Quantifies soft benefits like time savings and error reduction
- + Outputs Python script, CSV, and Markdown executive summary simultaneously
Cons
- - Model quality depends entirely on the accuracy of input assumptions
- - Does not integrate with live financial systems for actual cost data
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