Roi Estimator

ROI Estimator Skill

Every "should we spend on this?" decision needs a defensible number. This skill estimates the return — ROI %, payback period, and a simple NPV that accounts for the time value of money — from costs and expected benefits, with the assumptions stated and a sensitivity check, so a business case survives the first sceptical question instead of collapsing.

Required Inputs

Ask for these only if they aren't already provided:

• Costs — upfront cost, and any ongoing/recurring cost (per period).
• Benefits — the expected gain per period (revenue uplift, cost saved, time saved × loaded rate). Quantify; if it's an estimate, say so.
• Time horizon — over how many periods to evaluate (e.g. 3 years).
• Discount rate — for NPV (default ~10%); state it.

Output Format

ROI: [investment]

1. The numbers (via the helper):

| Metric | Value | |---|---| | Total cost (over horizon) | | | Total benefit (over horizon) | | | Net benefit | | | ROI % | | | Payback period | | | Simple NPV (@ discount rate) | |

2. The verdict — worth it / marginal / no, in one line, and against what bar (e.g. beats the discount-rate hurdle, payback within tolerance).

3. Assumptions — list them explicitly. The benefit is usually the soft number — flag it, and give a conservative / expected / optimistic range rather than a single point.

4. Sensitivity — the one assumption the conclusion hinges on, and at what value the decision flips.

Programmatic Helper

scripts/roi.py (stdlib only) computes ROI, payback, and NPV:

# in.json: {"upfront_cost":50000,"recurring_cost":2000,"benefit_per_period":18000,"periods":36,"discount_rate_annual":0.1,"period":"month"}
python3 scripts/roi.py in.json
python3 scripts/roi.py in.json --json

Quality Checks

• [ ] Costs include recurring/ongoing, not just upfront
• [ ] NPV is computed (time value of money), not just raw ROI
• [ ] Benefits are given as a range (conservative/expected/optimistic), not a single optimistic point
• [ ] Every assumption is listed explicitly
• [ ] A sensitivity note names the assumption the verdict hinges on and its flip point

Anti-Patterns

• [ ] Do not ignore ongoing costs — a low upfront, high-recurring option can lose to a pricier one-time buy
• [ ] Do not present a single benefit number as fact — it's the softest input; give a range and flag it
• [ ] Do not skip discounting for multi-year cases — $1 in year 3 isn't $1 today
• [ ] Do not bury the assumptions — a business case is only as credible as its stated inputs
• [ ] Do not omit payback — a great 5-year ROI with a 4-year payback may still be too slow to fund

Based On

Business-case / capital-budgeting practice — ROI, payback period, NPV, and assumption sensitivity.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: mohitagw15856
• Source: mohitagw15856/pm-claude-skills
• License: MIT
• Homepage: https://mohitagw15856.github.io/pm-claude-skills/

Install and usage instructions live in the source repository linked above.