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 vasilyu1983/AI-Agents-public --skill foundations-game-theorygit clone --depth 1 https://github.com/vasilyu1983/AI-Agents-publicWrote 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/vasilyu1983/ai-agents-public/foundations-game-theory)<a href="https://agentmods.dev/skills/vasilyu1983/ai-agents-public/foundations-game-theory"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/foundations-game-theory/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/vasilyu1983/ai-agents-public/foundations-game-theory"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/foundations-game-theory.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 Excessive Agency · line 216 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00037 | $0.09931 |
| Opus 5 | $0.00018 | $0.04966 |
| Sonnet 5 | $0.00007 | $0.01986 |
| Haiku 4.5 | $0.00004 | $0.00993 |
Grade A, and why
foundations-game-theory 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 — 392 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Game Theory Foundations
22 applied game-theory primitives for strategic decision systems, backed by a formal theory map. Each applied primitive solves a specific incentive or coordination failure. Primitives are domain-agnostic: the same mechanism that prevents free-riding in agent teams prevents cost-shifting in partnership contracts; the same auction that routes tasks routes ad placements.
For the agent-team applied recipe layer (team.yaml manifest fields, agent-team anti-patterns, agent-team decision checklist, composition recipes for typical agent-team scenarios), see agents-subagents/references/game-theory-agent-teams.md.
Contents
- Quick Reference
- Primitive Index
- Formal Supporting Theory
- Expert Judgment: When the Model Helps vs Misleads
- Anti-Patterns
- Misuse Boundaries
- Decision Checklist
- Composition Recipes
- Workflow
- ASCII Flow
- Current Pattern Review
- Navigation
- Fact-Checking
Quick Reference
| Primitive | Domain | Recipe Stub |
|---|---|---|
| Belief-Driven Coordination (ECON) | Multi-party teams, distributed analysis, agent teams | Members optimize against beliefs about co-members; reduces redundant work and inter-member chat |
| Adversarial Debate | Content moderation, risk review, audit | Two heterogeneous evaluators + reasoning-tree synthesis; no majority vote |
| Auction-Based Routing | Ad placement, task delegation, resource allocation | Sealed-bid truthful auction; highest-value-per-cost wins |
| Shapley Contribution | Attribution, revenue sharing, team composition | Marginal-contribution average across subsets |
| Reputation-Gated Autonomy | Supplier qualification, agent oversight, fraud gating | Tiered trust: proven → standard → probationary; oversight inversely proportional |
| Cooperation and Defection | Partnership design, incentive alignment, compliance | Iterated PD structure; payoff-scale to detect defection tendency |
| Mechanism Design for Synthesis | Decision aggregation, voting, policy-making | Vickrey truthful-revelation; dissent is a required section |
| Courtroom-Style Debate | Legal review, risk go/no-go, claim verification | Plaintiff/defense/court structure + progressive RAG + role-switching |
| Pareto-Nash Multi-Objective | Product tradeoffs, regulatory vs growth, pricing tiers | Map Pareto frontier; pick dominant options; flag non-dominated set |
| Evolutionary Coordination Search | Algorithm selection, prompt tuning, rule evolution | LLM-mutated program + fitness signal; ShinkaEvolve for sample efficiency |
| Prediction Market Confidence | Forecasting, risk calibration, hiring decisions | Stake-weighted confidence; CritiCal calibration step before stake |
| Negotiation ZOPA/BATNA | Pricing, partnership terms, resource contention | Map BATNA/ZOPA per party; target overlap zone; use interests not positions |
| Reasoning-Tree Audit | High-stakes synthesis, compliance review, claim checking | Trace claims to evidence at First Point of Disagreement; reject unsupported majority |
| Per-Claim Credibility Scoring | Misinformation detection, adversarial content, security | Evidence quality × corroboration weight per claim; isolate high-risk claims |
| Generative Social Choice | Multi-stakeholder policy, diverse-user product decisions | Maximin selection across candidate outputs; preserve minority-signal coverage |
| Meta-Debate Role Routing | Debate setup, role-fit selection, agent teams | Two-stage proposal + peer-review picks plaintiff/defense/judge from a pool |
| Online Shapley Prompt Evolution | High-frequency teams, prompt tuning over many runs | Per-member prompt mutation guided by Shapley contribution (HiveMind) |
| Beyond Majority Voting (BMV) | Best-of-N synthesis (discrete answer), ensemble selection | Optimal Weight (confidence × calibration) + Inverse Surprising Popularity |
| Radial Consensus Score (RCS) | Best-of-N synthesis (open-ended generation), self-consistency | Embedding-centroid selector for semantically clustered, lexically diverse answers |
| Conformal Social Choice | High-stakes debate verdicts, act/escalate gates | Calibrated prediction set: singleton acts, multi-answer set escalates |
| Attested Delegation Contracts | Cross-trust subagent routing, agent marketplaces, external tools | Route by verified capability and bounded contract, not self-claimed quality |
| Coalition Formation Routing | Large teams, departments, multi-workstream audits | Form stable subteams before synthesis; avoid flat-panel overload |
What ships with it
30 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/openai.yaml 301 B
- assets/templates/game-theory/01-econ-belief-driven.md 2.4 KB
- assets/templates/game-theory/02-adversarial-debate.md 4.6 KB
- assets/templates/game-theory/03-auction-task-routing.md 3.6 KB
- assets/templates/game-theory/04-shapley-contribution.md 4.2 KB
- assets/templates/game-theory/05-reputation-gating.md 3.8 KB
- assets/templates/game-theory/06-cooperation-defection.md 8.6 KB
- assets/templates/game-theory/07-mechanism-design-synthesis.md 4.6 KB
- assets/templates/game-theory/08-courtroom-proclaim.md 3.8 KB
- assets/templates/game-theory/09-pareto-nash.md 4.3 KB
- assets/templates/game-theory/10-alphaevolve.md 4.3 KB
- assets/templates/game-theory/11-prediction-market.md 4.8 KB
- assets/templates/game-theory/12-negotiation-zopa-batna.md 2.7 KB
- assets/templates/game-theory/13-reasoning-tree-audit.md 7.7 KB
- assets/templates/game-theory/14-credibility-scoring.md 7.2 KB
- assets/templates/game-theory/15-generative-social-choice.md 6.2 KB
- assets/templates/game-theory/16-meta-debate-routing.md 5.0 KB
- assets/templates/game-theory/17-online-shapley-prompt-evolution.md 4.7 KB
- assets/templates/game-theory/18-beyond-majority-voting.md 3.8 KB
- assets/templates/game-theory/19-radial-consensus-score.md 3.6 KB
- assets/templates/game-theory/20-conformal-social-choice.md 3.9 KB
- assets/templates/game-theory/21-attested-delegation-contracts.md 3.5 KB
- assets/templates/game-theory/22-coalition-formation-routing.md 3.4 KB
- assets/templates/game-theory/README.md 8.5 KB
- data/sources.json 35 KB
- learnings.consolidated.md 599 B
- learnings.md 368 B
- references/formal-theory-map.md 17 KB
- references/patterns-scenarios-traps.md 15 KB
- references/primitives-overview.md 17 KB
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 · 392 lines · 37 tokens per session scan A 70821d416625
foundations-game-theory is a skill published in the GitHub repository vasilyu1983/AI-Agents-public (87 stars, last pushed 10d ago), licensed MIT. It adds 37 tokens to every session and 9,931 once invoked, about $0.0002 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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