# Send Game Theory to your agent Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually. ## Fast path - Download the package from Yavira. - Extract it into a folder your agent can access. - Paste one of the prompts below and point your agent at the extracted folder. ## Suggested prompts ### New install ```text I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete. ``` ### Upgrade existing ```text I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run. ``` ## Machine-readable fields ```json { "schemaVersion": "1.0", "item": { "slug": "game-theory", "name": "Game Theory", "source": "tencent", "type": "skill", "category": "AI 智能", "sourceUrl": "https://clawhub.ai/sp0oby/game-theory", "canonicalUrl": "https://clawhub.ai/sp0oby/game-theory", "targetPlatform": "OpenClaw" }, "install": { "downloadUrl": "/downloads/game-theory", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=game-theory", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "packageFormat": "ZIP package", "primaryDoc": "SKILL.md", "includedAssets": [ "SKILL.md", "examples/uniswap-v3-analysis.md", "references/auction-theory.md", "references/governance-attacks.md", "references/information-economics.md", "references/liquidity-games.md" ], "downloadMode": "redirect", "sourceHealth": { "source": "tencent", "slug": "game-theory", "status": "healthy", "reason": "direct_download_ok", "recommendedAction": "download", "checkedAt": "2026-05-01T15:47:19.161Z", "expiresAt": "2026-05-08T15:47:19.161Z", "httpStatus": 200, "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=game-theory", "contentType": "application/zip", "probeMethod": "head", "details": { "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=game-theory", "contentDisposition": "attachment; filename=\"game-theory-1.0.0.zip\"", "redirectLocation": null, "bodySnippet": null, "slug": "game-theory" }, "scope": "item", "summary": "Item download looks usable.", "detail": "Yavira can redirect you to the upstream package for this item.", "primaryActionLabel": "Download for OpenClaw", "primaryActionHref": "/downloads/game-theory" }, "validation": { "installChecklist": [ "Use the Yavira download entry.", "Review SKILL.md after the package is downloaded.", "Confirm the extracted package contains the expected setup assets." ], "postInstallChecks": [ "Confirm the extracted package includes the expected docs or setup files.", "Validate the skill or prompts are available in your target agent workspace.", "Capture any manual follow-up steps the agent could not complete." ] } }, "links": { "detailUrl": "https://openagent3.xyz/skills/game-theory", "downloadUrl": "https://openagent3.xyz/downloads/game-theory", "agentUrl": "https://openagent3.xyz/skills/game-theory/agent", "manifestUrl": "https://openagent3.xyz/skills/game-theory/agent.json", "briefUrl": "https://openagent3.xyz/skills/game-theory/agent.md" } } ``` ## Documentation ### Game Theory for Crypto Strategic analysis framework for understanding and designing incentive systems in web3. "Every protocol is a game. Every token is an incentive. Every user is a player. Understand the rules, or become the played." ### When to Use This Skill Analyzing tokenomics for exploits or misaligned incentives Evaluating governance proposals and voting mechanisms Understanding MEV and adversarial transaction ordering Designing auction mechanisms (NFT drops, token sales, liquidations) Predicting how rational actors will behave in a system Identifying attack vectors in DeFi protocols Modeling liquidity provision strategies Assessing protocol sustainability ### The Five Questions For any protocol or mechanism, ask: Who are the players? (Users, LPs, validators, searchers, governance token holders) What are their strategies? (Actions available to each player) What are the payoffs? (How does each outcome affect each player?) What information do they have? (Complete, incomplete, asymmetric?) What's the equilibrium? (Where do rational actors end up?) ### Analysis Template ## Protocol: [Name] ### Players - Player A: [Role, objectives, constraints] - Player B: [Role, objectives, constraints] - ... ### Strategy Space - Player A can: [List possible actions] - Player B can: [List possible actions] ### Payoff Structure - If (A does X, B does Y): A gets [payoff], B gets [payoff] - ... ### Information Structure - Public information: [What everyone knows] - Private information: [What only some players know] - Observable actions: [What can be seen on-chain] ### Equilibrium Analysis - Nash equilibrium: [Stable outcome where no player wants to deviate] - Dominant strategies: [Strategies that are always best regardless of others] - Potential exploits: [Deviations that benefit attackers] ### Recommendations - [Design changes to improve incentive alignment] ### Reference Documents DocumentUse CaseNash EquilibriumFinding stable outcomes in strategic interactionsMechanism DesignDesigning systems with desired equilibriaAuction TheoryToken sales, NFT drops, liquidationsMEV Game TheoryAdversarial transaction orderingTokenomics AnalysisEvaluating token incentive structuresGovernance AttacksVoting manipulation and captureLiquidity GamesLP strategies and impermanent lossInformation EconomicsAsymmetric information and signaling ### Nash Equilibrium A state where no player can improve their payoff by unilaterally changing strategy. The "stable" outcome of a game. Crypto application: In a staking system, Nash equilibrium determines the stake distribution across validators. ### Dominant Strategy A strategy that's optimal regardless of what others do. Crypto application: In a second-price auction, bidding your true value is dominant. ### Pareto Efficiency An outcome where no one can be made better off without making someone worse off. Crypto application: AMM fee structures try to be Pareto efficient for traders and LPs. ### Mechanism Design "Reverse game theory" - designing rules to achieve desired outcomes. Crypto application: Designing token vesting schedules to align long-term incentives. ### Schelling Point A solution people converge on without communication. Crypto application: Why certain price levels act as psychological support/resistance. ### Incentive Compatibility When truthful behavior is optimal for participants. Crypto application: Oracle designs where honest reporting is the dominant strategy. ### Common Knowledge Everyone knows X, everyone knows everyone knows X, infinitely recursive. Crypto application: Public blockchain state creates common knowledge of balances/positions. ### Pattern 1: The Tragedy of the Commons Structure: Shared resource, individual incentive to overuse, collective harm. Crypto examples: Gas price bidding during congestion Governance token voting apathy MEV extraction degrading UX Solution approaches: Harberger taxes Quadratic mechanisms Commitment schemes ### Pattern 2: The Prisoner's Dilemma Structure: Individual rationality leads to collective irrationality. Crypto examples: Liquidity mining mercenaries (farm and dump) Race-to-bottom validator fees Bridge security (each chain wants others to secure) Solution approaches: Repeated games (reputation) Commitment mechanisms (staking/slashing) Mechanism redesign ### Pattern 3: The Coordination Game Structure: Multiple equilibria, players want to coordinate but may fail. Crypto examples: Which L2 to use? Token standard adoption Hard fork coordination Solution approaches: Focal points (Schelling points) Sequential moves (first mover advantage) Communication mechanisms ### Pattern 4: The Principal-Agent Problem Structure: One party acts on behalf of another with misaligned incentives. Crypto examples: Protocol team vs token holders Delegates in governance Fund managers Solution approaches: Incentive alignment (token vesting) Monitoring (transparency) Bonding (skin in game) ### Pattern 5: Adverse Selection Structure: Information asymmetry leads to market breakdown. Crypto examples: Token launches (team knows more than buyers) Insurance protocols (risky users more likely to buy) Lending (borrowers know their risk better) Solution approaches: Signaling (lock-ups, audits) Screening (credit scores, history) Pooling equilibria ### Pattern 6: Moral Hazard Structure: Hidden action after agreement leads to risk-taking. Crypto examples: Protocols with insurance may take more risk Bailout expectations encourage leverage Anonymous teams may rug Solution approaches: Monitoring and transparency Incentive alignment Reputation systems ### The MEV Game Players: Users, searchers, builders, validators Key insight: Transaction ordering is a game; users are often the losers See: MEV Strategies ### The Liquidity Game Players: LPs, traders, arbitrageurs Key insight: Impermanent loss is the cost of being adversely selected against See: Liquidity Games ### The Governance Game Players: Token holders, delegates, protocol team Key insight: Rational apathy + concentrated interests = capture See: Governance Attacks ### The Staking Game Players: Stakers, validators, delegators Key insight: Security budget must exceed attack profit See: Tokenomics Analysis ### The Oracle Game Players: Data providers, consumers, attackers Key insight: Profit from manipulation must be less than cost See: Mechanism Design ### Tokenomics Red Flags Insiders can sell before others (vesting asymmetry) Inflation benefits few, dilutes many No sink mechanisms (perpetual selling pressure) Rewards without risk (free money = someone else paying) ### Governance Red Flags Low quorum thresholds (minority capture) No time delay (flash loan attacks) Token voting only (plutocracy) Delegates with no skin in game ### Mechanism Red Flags First-come-first-served (bot advantage) Sealed bids without commitment (frontrunning) Rebates/refunds (MEV extraction) Complex formulas (hidden exploits) ### Repeated Games and Reputation Single-shot games often have bad equilibria. Repetition enables cooperation through: Trigger strategies (cooperate until defection) Reputation building (costly to destroy) Future value (patient players cooperate more) Crypto application: Why anonymous actors behave worse than doxxed teams. ### Evolutionary Game Theory Strategies that survive competitive selection. Relevant for: Which protocols survive long-term Memetic competition between narratives Bot strategy evolution ### Bayesian Games Games with incomplete information. Players have beliefs about others' types. Crypto application: Trading with unknown counterparties, evaluating anonymous teams. ### Cooperative Game Theory When players can form binding coalitions. Crypto application: MEV extraction coalitions, validator cartels, governance blocs. ### Algorithmic Game Theory Computational aspects of game theory. Crypto application: On-chain game computation limits, gas-efficient mechanism design. ### Step 1: Model the Game Identify all players (including those not obvious) Map complete strategy spaces Define payoff functions precisely Specify information structure ### Step 2: Find Equilibria Check for dominant strategies Compute Nash equilibria Identify Pareto improvements Consider trembling-hand perfection ### Step 3: Stress Test What if players collude? What if new players enter? What if information leaks? What if parameters change? ### Step 4: Recommend Mechanism changes to improve equilibrium Monitoring to detect deviations Parameter bounds to maintain stability ### Foundational Texts "Theory of Games and Economic Behavior" - von Neumann & Morgenstern "A Beautiful Mind" (Nash's life, accessible intro) "The Strategy of Conflict" - Schelling "Mechanism Design Theory" - Myerson (Nobel lecture) ### Crypto-Specific "Flash Boys 2.0" - MEV paper "SoK: DeFi Attacks" - Systemization of DeFi exploits "Clockwork Finance" - MEV and mechanism design Paradigm research blog ### Tools Nashpy (Python game theory library) Gambit (game theory software) Agent-based modeling frameworks ## Trust - Source: tencent - Verification: Indexed source record - Publisher: sp0oby - Version: 1.0.0 ## Source health - Status: healthy - Item download looks usable. - Yavira can redirect you to the upstream package for this item. - Health scope: item - Reason: direct_download_ok - Checked at: 2026-05-01T15:47:19.161Z - Expires at: 2026-05-08T15:47:19.161Z - Recommended action: Download for OpenClaw ## Links - [Detail page](https://openagent3.xyz/skills/game-theory) - [Send to Agent page](https://openagent3.xyz/skills/game-theory/agent) - [JSON manifest](https://openagent3.xyz/skills/game-theory/agent.json) - [Markdown brief](https://openagent3.xyz/skills/game-theory/agent.md) - [Download page](https://openagent3.xyz/downloads/game-theory)