Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
What This Skill Does
A structured framework for applying game theory to crypto protocol analysis. Covers tokenomics evaluation, MEV dynamics, governance mechanism design, and adversarial behavior modeling. Includes templates, common pattern libraries, and step-by-step methodology for finding equilibria in DeFi systems.
Applies formal game theory directly to crypto-specific scenarios with pre-built patterns for MEV, liquidity games, governance attacks, and oracle design, rather than requiring the analyst to translate general theory into web3 context from scratch.
When to use it
- Auditing a new DeFi protocol's tokenomics for misaligned incentives before investing
- Modeling validator behavior in a proof-of-stake network to assess attack feasibility
- Identifying governance capture risks in a DAO ahead of a major proposal vote
- Evaluating MEV exposure in a protocol's transaction ordering mechanism
- Designing auction parameters for an NFT mint or token sale
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: Analyze the incentive structure of a staking protocol where validators earn 5% APY but face slashing for downtime
- 1Identifies all players: stakers, validators, delegators, and the protocol itself
- 2Maps each player's strategy space, including stake allocation, uptime decisions, and delegation choices
- 3Defines payoff functions and checks whether the security budget exceeds potential attack profit
- 4Runs equilibrium analysis to find the Nash equilibrium stake distribution across validators
- 5Flags red flags such as vesting asymmetry or slashing penalties too low to deter attacks
Structured report with equilibrium prediction, identified attack vectors, and recommended parameter changes to keep validator behavior honest