s4h-game-theory-mechanism-design

s4h-game-theory-mechanism-design is a skill for Claude Code from human-avatar/skills-for-humanity. It costs 98 tokens per session (1,761 once invoked), scanned A, original, MIT.

A guide to mechanism design, sometimes called reverse game theory: designing rules so that self-interested people still produce a desired result. It focuses on situations where participants have information the rule-maker cannot directly see.

In plain words
What is it for?
Use it to design markets, voting systems, contracts, rewards, allocation rules, and processes that encourage truthful behaviour.
Why use it?
It helps address dishonest reporting, hidden effort, and conflicting incentives by changing the rules rather than simply asking people to behave better.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: names the AskUserQuestion tool.

Part of the skills-for-humanity plugin — 197 skills, 1 hook shipped together

Good fit Use it to design markets, voting systems, contracts, rewards, allocation rules, and processes that encourage truthful behaviour.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/human-avatar/skills-for-humanity/s4h-game-theory-mechanism-design
Install

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.

Any agent
npx skills add human-avatar/skills-for-humanity --skill s4h-game-theory-mechanism-design
Clone the repo
git clone --depth 1 https://github.com/human-avatar/skills-for-humanity

Made for: Claude Code.

Or install skills-for-humanity, the plugin that ships this one along with the rest of its 197 skills, 1 hook.

Wrote 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.

agentmods badge for s4h-game-theory-mechanism-design

README.md
[![agentmods](https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-game-theory-mechanism-design/github.svg)](https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-game-theory-mechanism-design)
Your own site
<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.

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Your own site · 80×15
<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>
Per session 98 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,761 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 9d ago against content hash dc0b8722dc14, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

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.

skills/s4h-game-theory-mechanism-design/SKILL.md · 124 lines

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

Read the full file on GitHub · 124 lines

Changes

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.

  1. 9d ago First seen · 124 lines · 98 tokens per session scan A dc0b8722dc14

Subscribe to this mod's changes

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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