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-game-theory-mechanism-designgit 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-game-theory-mechanism-design)<a href="https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-game-theory-mechanism-design"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-game-theory-mechanism-design/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-game-theory-mechanism-design"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-game-theory-mechanism-design.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 | $0.00098 | $0.01761 |
| Opus 5 | $0.00049 | $0.00881 |
| Sonnet 5 | $0.00020 | $0.00352 |
| Haiku 4.5 | $0.00010 | $0.00176 |
Grade A, and why
s4h-game-theory-mechanism-design 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 9d 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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Game Theory: Mechanism Design
Standard game theory takes the rules as given and asks what rational players will do. Mechanism design inverts this: it takes the desired outcome as given and asks what rules will produce it. This is why it is often called reverse game theory.
The central insight, formalised by Leonid Hurwicz and developed by Eric Maskin and Roger Myerson (who shared the 2007 Nobel Prize), is that private information is the root challenge. Players know things the designer doesn't — their true valuations, their effort levels, their costs — and they have incentives to misrepresent that information if doing so serves them. A well-designed mechanism elicits honest behaviour not by demanding honesty, but by making honesty the dominant strategy: the player's best move given the rules, regardless of what others do.
The revelation principle is the foundational theorem: any equilibrium of any mechanism can be replicated by a direct incentive-compatible mechanism — one where each player simply reports their private information truthfully and the rules process it correctly. This means the designer never needs to think about indirect or complicated mechanisms; there is always an honest, direct mechanism that achieves the same outcome.
William Vickrey's second-price auction is the canonical example: by having the winner pay the second-highest bid rather than their own, the dominant strategy becomes truthful bidding. The mechanism extracts honest valuations without demanding or relying on honesty.
Your Process
Step 1: Desired outcome State precisely what behaviour or allocation the mechanism should produce. Vague goals produce vague mechanisms. "People should behave better" is not a desired outcome. "Employees should report their true performance levels" is. "Suppliers should bid their true costs" is. Be specific about whose behaviour, what information, and what allocation.
Framing check: Confirm the desired outcome and the players involved before continuing. State what you've identified — the specific behaviour or allocation being targeted, the players whose incentives need aligning, and the private information at stake — in one sentence, then use AskUserQuestion:
- Question: "I'm reading this as: [your one-sentence framing of the desired outcome, the players, and the core misalignment]. 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.
- 9d ago First seen · 124 lines · 98 tokens per session scan A dc0b8722dc14
s4h-game-theory-mechanism-design is a skill published in the GitHub repository human-avatar/skills-for-humanity (223 stars, last pushed 1mo ago), licensed MIT. It adds 98 tokens to every session and 1,761 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 2026-09-03.
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