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aamas-reproducibility

Agent skill frombrycewang-stanford/Awesome-Journal-Skills

Use when strengthening AAMAS reproducibility evidence for interaction claims, including proofs and game definitions, opponent and population sets, self-play protocols, random seeds, compute, uncertainty on strategic outcomes, baselines, and consistency between what the paper claims about agents and what the artifact can actually show.

How to install aamas-reproducibility

install aamas-reproducibility
# install the Awesome-Journal-Skills pack with the skills CLI
npx skills add brycewang-stanford/Awesome-Journal-Skills

# or add only this skill to Claude Code: copy the folder

AAMAS-Skills/skills/aamas-reproducibility/

# into

~/.claude/skills/aamas-reproducibility/ (all projects)

.claude/skills/aamas-reproducibility/ (one project)

AAMAS-Skills/skills/aamas-reproducibility/SKILL.md

MITFull file
1# AAMAS Reproducibility
2
3Use this before submission and again before camera-ready. The reproducibility question at AAMAS
4is not only "can I rerun the model" but "can I reproduce the *interaction* - the same agents,
5the same game, the same emergent outcome."
6
7## Evidence map
8
9- Map each theorem, mechanism property, convergence claim, and empirical interaction claim to a
10 verifiable location in the paper, appendix, supplement, or artifact.
11- For theory, state the game, the information structure, the solution concept, assumptions,
12 proof dependencies, and failure modes clearly enough for a game theorist.
13- For experiments, report the environment, number of agents, opponent/population set, training
14 regime, evaluation opponents, metrics, hyperparameter ranges, chosen settings, seeds,
15 repeated runs, compute, and runtime.
16- For small or noisy strategic differences, add uncertainty: standard errors, confidence
17 intervals, or paired tests over seeds and over opponents.
18- Explain any missing code or environment honestly, and describe how a reader could reproduce
19 the interaction in principle.
20- Keep the artifact consistent with the paper; a claim the artifact cannot demonstrate is a
21 review-risk multiplier.
22
23## Claim-to-evidence audit table
24
25| Claim | Pure-theory answer | Learning-plus-game answer |
26|---|---|---|
27| Solution concept reached | Proof with the game and information structure stated | Plus convergence curves under other agents' adaptation |
28…

Excerpt shown under the repository's MIT license.

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