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-coalitiongit 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-coalition)<a href="https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-game-theory-coalition"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-game-theory-coalition/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-coalition"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-game-theory-coalition.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00094 | $0.01872 |
| Opus 5 | $0.00047 | $0.00936 |
| Sonnet 5 | $0.00019 | $0.00374 |
| Haiku 4.5 | $0.00009 | $0.00187 |
Grade A, and why
s4h-game-theory-coalition 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Game Theory: Coalition Analysis
Cooperative game theory asks a different question from strategic (non-cooperative) game theory. Rather than asking what rational self-interested players will do when they can't coordinate, it asks: when players can form binding agreements and share gains, which coalitions will form, and how should the value be divided?
Lloyd Shapley's answer to the division question — the Shapley value (1953, Nobel Prize 2012) — is remarkable for its mathematical precision and moral intuition. Each player's fair share is their average marginal contribution across all possible orderings of coalition formation. Formally: for each permutation of all players, calculate how much value player i adds when they join the coalition that has formed before them. Average this marginal contribution across all permutations. The result is the Shapley value — the uniquely fair allocation given four axioms: efficiency (the grand coalition's total value is fully distributed), symmetry (identical players receive equal shares), dummy (players who contribute nothing receive nothing), and additivity (allocations across independent games add correctly).
The core captures coalition stability: an allocation is in the core if no subset of players can collectively do better by breaking away and forming their own coalition. If an allocation is in the core, no group has an incentive to defect — the grand coalition is stable. If the core is empty, no allocation is fully stable and some defection pressure is unavoidable.
These two concepts are complementary but distinct. The Shapley value is always unique and always exists — it answers "what is fair?" The core may be empty — it answers "what is stable?"
Your Process
Step 1: Player-value map List all players and, for each possible coalition (every subset), specify the value that coalition can generate on its own. This is the characteristic function of the game — v(S) for every subset S. For small groups (3–4 players), enumerate all subsets. For larger groups, focus on the most relevant coalitions: the grand coalition, each individual player alone, and the likely competing sub-coalitions.
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 · 123 lines · 94 tokens per session scan A 4480c01c627f
s4h-game-theory-coalition is a skill published in the GitHub repository human-avatar/skills-for-humanity (223 stars, last pushed 1mo ago), licensed MIT. It adds 94 tokens to every session and 1,872 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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