Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/adimango/ai-adoption-playbooknpx agentmods add skills/adimango/ai-adoption-playbook/roi-calculatorWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/adimango/ai-adoption-playbook/roi-calculator)<a href="https://agentmods.dev/skills/adimango/ai-adoption-playbook/roi-calculator"><img src="https://agentmods.dev/badge/skills/adimango/ai-adoption-playbook/roi-calculator/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/adimango/ai-adoption-playbook/roi-calculator"><img src="https://agentmods.dev/badge/skills/adimango/ai-adoption-playbook/roi-calculator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00031 | $0.03371 |
| Opus 5 | $0.00015 | $0.01685 |
| Sonnet 5 | $0.00006 | $0.00674 |
| Haiku 4.5 | $0.00003 | $0.00337 |
Grade A, and why
roi-calculator scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 235 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ROI Calculator
Purpose
Produces a board-ready ROI calculation from the founder's actual data — not industry benchmarks. Separates ROI into four dimensions (cost efficiency, revenue optimization, new revenue, capacity gained) so the founder can tell a complete story. This is a calculation tool, not a strategy session.
Core principle: Use the founder's real numbers. If a number is estimated, label it as estimated. Never substitute industry averages for missing data — flag the gap instead.
Important: This skill helps leaders calculate and present their own numbers — it does not audit or guarantee them. Figures going to a board, CFO, or investor should be validated by the company's finance owner first.
Context Intake
Unfamiliar
~~categoryplaceholders? See CONNECTORS.md for connected-tool categories.
Accept the input artifact in any form: a file path, pasted text, an attachment, or output from a skill run earlier in this conversation. If ~~cloud storage is connected, offer to fetch it from there.
If no artifact is provided: this skill builds on the fluency scorecard — offer to run fluency-assessment first, or proceed with the leader's verbal answers, clearly marking the output as based on self-reported data.
For Department: and Currency:, use the first available source: the scorecard → adoption.local.md (the department this run covers; by default the one marked (primary) — see CLAUDE.md Local Configuration) → ask the leader (currency defaults to USD). If the config lists multiple departments or whole org and no scorecard pins this run to one, confirm which department (or org-wide/Generic) before producing numbers.
Four ROI Dimensions
Every AI investment produces value across one or more of these dimensions. The fourth, Capacity Gained, is the question every CFO is now asking: did revenue grow faster than headcount?
| Dimension | What it measures | Examples |
|---|---|---|
| Cost efficiency | Time saved, spend reduced | Hours saved per active user per week, reduced contractor or agency spend, fewer tools needed |
| Revenue optimization | Existing revenue protected or grown | Faster feature shipping, reduced churn from faster bug fixes, shorter sales cycles |
| New revenue | Revenue that wouldn't exist without AI | AI-powered product features, new service offerings, markets entered faster |
| Capacity gained | Revenue growth on flat or shrinking FTE base | Revenue per FTE delta (e.g. +18% revenue / flat headcount), output per FTE, work absorbed without headcount growth |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago Changed fe85e7d04fe1
- 11d ago First seen · 235 lines · 31 tokens per session scan A 669a0c1a5b88
roi-calculator is a skill published in the GitHub repository adimango/ai-adoption-playbook (23 stars, last pushed 4d ago), licensed MIT. It adds 31 tokens to every session and 3,371 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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