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 human-avatar/skills-for-humanity --skill s4h-economics-incentive-mappinggit clone --depth 1 https://github.com/human-avatar/skills-for-humanityWrote 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/human-avatar/skills-for-humanity/s4h-economics-incentive-mapping)<a href="https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-economics-incentive-mapping"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-economics-incentive-mapping/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/human-avatar/skills-for-humanity/s4h-economics-incentive-mapping"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-economics-incentive-mapping.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 | $0.00095 | $0.01635 |
| Opus 5 | $0.00048 | $0.00817 |
| Sonnet 5 | $0.00019 | $0.00327 |
| Haiku 4.5 | $0.00010 | $0.00163 |
Grade A, and why
s4h-economics-incentive-mapping 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 12d 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 — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Economics: Incentive Mapping
Charlie Munger's dictum: "Show me the incentive and I'll show you the outcome." The most reliable predictor of behaviour is not what people intend, say they'll do, or are instructed to do — it is what the structure rewards them for doing. People are not irrational or immoral when they respond to incentives; they are responding rationally to the environment they're in. If behaviour is wrong, the problem is usually the incentive structure, not the people.
Incentive mapping is systematic: identify every party whose behaviour matters, determine what each party gains and loses under the current arrangement, predict the behaviours those incentives produce, and compare predicted behaviour to desired behaviour. The gap between predicted and desired is the misalignment — and misalignment is almost always the source of dysfunction in organisations, policies, and markets.
This framework draws on classical price theory but its most influential modern application is in principal-agent analysis: what happens when the person making decisions (the agent) has different incentives from the person bearing the consequences (the principal)? Kahneman's work on loss aversion adds a further layer — people respond more strongly to potential losses than to equivalent gains, so incentive systems that rely on upside alone are systematically weaker than those that also activate loss aversion.
Your Process
Step 1: Map the parties Identify every party in the system whose behaviour matters to the outcome. This includes: decision-makers, implementers, beneficiaries, those who bear costs, and third parties who are affected but have no formal role. Do not limit the list to the obvious actors — the most important incentives often belong to parties one step removed from the main action.
Framing check: Confirm the system and the parties before continuing. State what you've identified in one sentence, then use AskUserQuestion:
- Question: "I'm reading this as: [one-sentence framing — the system being examined and the parties whose behaviour matters]. Is that right?"
- Header: "Framing"
- Options:
- Yes — proceed — framing is correct
- Adjust — one element is off; user will correct it before you continue
- Reframe — different situation than read; incorporate the correction before proceeding
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.
- 12d ago First seen · 101 lines · 95 tokens per session scan A 830e4484dfca
s4h-economics-incentive-mapping is a skill published in the GitHub repository human-avatar/skills-for-humanity (223 stars, last pushed 1mo ago), licensed MIT. It adds 95 tokens to every session and 1,635 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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