Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add magnus919/agent-skills --skill ai-operating-economicsgit clone --depth 1 https://github.com/magnus919/agent-skillsWrote 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/magnus919/agent-skills/ai-operating-economics)<a href="https://agentmods.dev/skills/magnus919/agent-skills/ai-operating-economics"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/ai-operating-economics/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/magnus919/agent-skills/ai-operating-economics"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/ai-operating-economics.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00091 | $0.03931 |
| Opus 5 | $0.00046 | $0.01965 |
| Sonnet 5 | $0.00018 | $0.00786 |
| Haiku 4.5 | $0.00009 | $0.00393 |
Grade A, and why
ai-operating-economics 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 13d 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 — 261 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Operating Economics
Overview
AI initiatives are operating interventions, not merely model purchases or ROI spreadsheets. Their value depends on what work changes, who benefits, what quality or risk changes with it, what the complete intervention costs, and whether the organization can observe and govern those changes.
This skill provides the cross-domain decision spine for evaluating an AI-enabled workflow. It does not replace financial modeling, product measurement, statistical inference, agent evaluation, runtime operations, or AI governance. It makes those inputs meet in one accountable decision record.
The core question is not “Did the model make people faster?” It is: “What changed in this workflow, for whom, at what full cost, with what outcome and countermetric evidence, and what authority should the organization grant next?”
Entry Points
| Starting state | Start with | Primary artifact or route |
|---|---|---|
| Idea or proposed AI workflow | Steps 1–2 | templates/ai-initiative-evidence-record.md |
| Existing pilot or outcome data | Steps 3–7 | references/evidence-method.md plus the evidence record |
| Request for broader population or side-effect authority | Steps 7–8; load references/evidence-method.md section 7a for the governance packet |
Governance evidence packet plus the evidence record |
| Executive, portfolio, launch, or lifecycle review | Steps 8–9 | templates/ai-economics-review.md; route launch/runtime details onward |
| Standalone financial, statistical, telemetry, runtime, or governance implementation task | When Not to Use | Named adjacent specialist skill |
When to Use
Load this skill when the user needs to:
- Build an evidence-backed business case for an AI use case or agentic workflow.
- Decide whether an AI pilot should scale, remain bounded, be redesigned, or stop.
- Review claimed AI productivity, savings, adoption, or transformation results.
- Design an AI value-realization or post-launch outcome review.
- Connect model and tool spend to workflow outcomes and worker or customer effects.
- Compare AI options while accounting for measurement uncertainty and non-comparable evidence.
- Prepare an executive, product, portfolio, or lifecycle decision about an AI-enabled intervention.
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 13d ago First seen · 261 lines · 91 tokens per session scan A c99e03600cd5
ai-operating-economics is a skill published in the GitHub repository magnus919/agent-skills (76 stars, last pushed 2d ago), licensed MIT. It adds 91 tokens to every session and 3,931 once invoked, about $0.0005 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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