Use when designing or auditing AAMAS experiments - self-play and population-based training, opponent selection, equilibrium and regret metrics, game-theoretic simulations, ablations, seeds, hyperparameters, compute, and claim-to-evidence fit - with emphasis on experiments that probe the interaction rather than chase a single-agent leaderboard.
no risky patterns detected
Type: Agent skill (SKILL.md) · Category: Agent Workflows & Orchestration · Source: https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/HEAD/AAMAS-Ski
| Capabilities (fewer powerful permissions = higher) | 50/50 |
| Maintenance | 35/35 |
| Documentation | 15/15 |
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