Agent Skillsbrycewang-stanford/Awesome-Journal-Skills › aamas-artifact-evaluation

aamas-artifact-evaluation

GitHub

用于AAMAS论文的多智能体代码、环境及日志匿名打包,确保交互声明可复现。涵盖游戏定义、对手集、种子及收益日志的整理,支持审稿人快速验证收敛性、合作性或机制真实性等核心主张。

AAMAS-Skills/skills/aamas-artifact-evaluation/SKILL.md brycewang-stanford/Awesome-Journal-Skills

Trigger Scenarios

需要为AAMAS论文准备多智能体系统的可复现证据包 需要将代码、环境和日志匿名化以便同行评审或公开发布

Install

npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aamas-artifact-evaluation -g -y
More Options

Non-standard path

npx skills add https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/AAMAS-Skills/skills/aamas-artifact-evaluation -g -y

Use without installing

npx skills use brycewang-stanford/Awesome-Journal-Skills@aamas-artifact-evaluation

指定 Agent (Claude Code)

npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aamas-artifact-evaluation -a claude-code -g -y

安装 repo 全部 skill

npx skills add brycewang-stanford/Awesome-Journal-Skills --all -g -y

预览 repo 内 skill

npx skills add brycewang-stanford/Awesome-Journal-Skills --list

SKILL.md

Frontmatter
{
    "name": "aamas-artifact-evaluation",
    "description": "Use when packaging AAMAS multiagent code, environments, opponent and population sets, random seeds, game definitions, and logs as anonymous supplementary evidence or a public post-acceptance release, even without a separate artifact badge, so that game-theory and MARL reviewers can inspect and re-run the interaction claims."
}

AAMAS Artifact Evaluation

Use this for evidence packaging around AAMAS. Because the venue is about interaction, an artifact must make a multiagent claim inspectable: the game, the other agents, and the protocol, not just a single trained model.

Artifact plan

  • Decide what a reviewer needs to believe the interaction claim: game or environment code, opponent/population definitions, the training regime, seeds, payoff logs, proofs, or qualitative episode traces.
  • Keep decision-critical evidence in the main paper or appendix; optional bulk runs can live in the supplementary zip.
  • Anonymize repository history, paths, environment names, license headers, cluster paths, and commit authors for the review version.
  • Include a minimal reproduction map: environment build, dependencies, hardware, commands, expected outputs, per-run wall-clock, seeds, and known nondeterminism (especially in self-play).
  • For a deployed or human-subject setting, give enough provenance for credible reproduction without violating data-use terms.
  • After acceptance, replace anonymous archives with a public, licensed, citable artifact.

What AAMAS evidence reviewers open first

The single fact that shapes packaging: a reviewer will re-run a small game far sooner than they will retrain a large policy, so make the strategic core turnkey before polishing anything.

Claim type First artifact inspected Common failure caught
Convergence to an equilibrium The game definition and the learning-rule code Solution concept named in the paper but not encoded in the evaluation
Emergent cooperation/defection The environment and reward specification Result depends on an undocumented reward-shaping constant
Beats other agents The opponent/population set and match protocol Only self-play reported; no held-out opponents
Mechanism is truthful The payment rule plus a strategic-deviation test No script that lets an agent try to game the mechanism

Worked vignette: packaging a self-play study

A hypothetical submission claims a learning rule that converges to a correlated equilibrium in a repeated congestion game, shown by self-play.

  • Ship the game as one parameterized generator (number of agents, capacity, payoff scale) rather than constants buried in a notebook, so reviewers can vary the interaction.
  • Record the exact seed sequence and replication count behind every convergence plot; an equilibrium-convergence claim without seeds is unfalsifiable.
  • Emit payoff and regret tables directly from logged results so PDF and artifact numbers cannot drift.
  • Include a strategic-deviation harness: a script that drops in a non-conforming agent and measures whether it profits, because that is exactly what a game-theory reviewer will try.

Calibration anchors

  • Supplement inspection at AAMAS is at reviewer discretion; assume only the README and one entry script get opened, and design the top level accordingly.
  • Supplement size and format caps vary by cycle (25 MB single zip in 2026); verify against the current OpenReview form rather than a past year.

Output format

[Artifact role] anonymous supplement / camera-ready release / public archive
[Contents] <game/env/opponents/seeds/proofs/logs>
[Anonymity risks] <paths/metadata/licenses/URLs>
[Reproduction level] turnkey / scripted / descriptive / weak
[Fixes before upload] <ordered list>

Version History

  • 9f86f09 Current 2026-07-19 14:15

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Version
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2026-07-19 14:15

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