s4h-game-theory-auction

s4h-game-theory-auction is a skill for Claude Code from human-avatar/skills-for-humanity. It costs 97 tokens per session (2,148 once invoked), scanned A, original, MIT.

An analysis of how people bid in auctions and how auctions can be designed. It covers situations such as sealed bids, where participants submit offers without seeing the others' offers, and the risk of paying more than an item is worth.

In plain words
What is it for?
Use it to plan a bid, compare auction formats, avoid overbidding, or design an auction for a target such as higher revenue or efficient allocation.
Why use it?
It helps distinguish bidding rules that reward honest valuations from those where bidders need to adjust their offers. It also addresses the winner's curse: winning because you overestimated the item's value.

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 plan a bid, compare auction formats, avoid overbidding, or design an auction for a target such as higher revenue or efficient allocation.

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

README.md
[![agentmods](https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-game-theory-auction/github.svg)](https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-game-theory-auction)
Your own site
<a href="https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-game-theory-auction"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-game-theory-auction/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.

agentmods 80×15 button for s4h-game-theory-auction

Your own site · 80×15
<a href="https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-game-theory-auction"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-game-theory-auction.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 97 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,148 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 $0.00097 $0.02148
Opus 5 $0.00048 $0.01074
Sonnet 5 $0.00019 $0.00430
Haiku 4.5 $0.00010 $0.00215

Measured 9d ago against content hash 959498f2f64d, 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-auction 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-auction/SKILL.md · 135 lines

How it starts

The opening of the file, as written. The whole thing — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Game Theory: Auction Analysis

William Vickrey's 1961 discovery is one of the cleanest results in economics: in a second-price sealed-bid auction, bidding your true value is a dominant strategy — the best move regardless of what others bid. The mechanism works because you pay the second-highest bid, not your own. Overbidding your true value doesn't help you (you might win but pay more than the item is worth); underbidding doesn't help you either (you might lose an item worth more than you'd have paid). So you bid your true value and let the second-highest bid determine the price. Vickrey received the Nobel Prize in 1996 for this result and related work.

First-price auctions are strategically different: you pay what you bid, so optimal play requires shading your bid below your true value. The optimal shade depends on the number of competitors (shade more with more competitors) and the distribution of their valuations (shade more when competition is intense). In equilibrium, first-price and second-price auctions generate the same expected revenue — the revenue equivalence theorem — under standard conditions.

The winner's curse is the most common failure mode in common-value auctions (where the item has an underlying objective value everyone is trying to estimate, rather than a private personal value). Winning means you bid highest, which means your estimate was the most optimistic among all bidders. In expectation, if you bid your unconditional estimate and win, you've overpaid — because winning reveals that you were the most optimistic, not the most accurate. The correct bid is your estimate conditional on winning, which is lower than your unconditional estimate.

Paul Milgrom and Robert Wilson (Nobel 2020) developed the modern theory of auction design, including the simultaneous ascending auction used in FCC spectrum allocation — showing how auction design directly affects both revenue and efficient allocation.


Your Process

Step 1: Auction type identification Identify the auction format:

  • First-price sealed bid: all bidders submit one bid simultaneously; highest bid wins and pays their own bid
  • Second-price sealed bid (Vickrey): highest bid wins but pays the second-highest bid
  • Ascending (English): price rises until only one bidder remains; winner pays the final price
  • Descending (Dutch): price falls from a high start until the first bidder claims the item at the current price
  • Other: procurement reverse auctions, combinatorial auctions, multi-round formats

Read the full file on GitHub · 135 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 · 135 lines · 97 tokens per session scan A 959498f2f64d

Subscribe to this mod's changes

s4h-game-theory-auction is a skill published in the GitHub repository human-avatar/skills-for-humanity (223 stars, last pushed 1mo ago), licensed MIT. It adds 97 tokens to every session and 2,148 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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