# Aamas Reproducibility

Use when strengthening AAMAS reproducibility evidence for interaction claims, including...

- Repository: https://github.com/brycewang-stanford/Awesome-Journal-Skills (1346 stars)
- Category: AI and agents
- License: MIT
- Last updated: 2026-09-27
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aamas-reproducibility -g -a claude-code -y`
Codex: `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aamas-reproducibility -g -a codex -y`
Cursor: `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aamas-reproducibility -g -a cursor -y`
Gemini CLI: `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aamas-reproducibility -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add brycewang-stanford/Awesome-Journal-Skills`

## Description

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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# AAMAS Reproducibility

Use this before submission and again before camera-ready. The reproducibility question at AAMAS
is not only "can I rerun the model" but "can I reproduce the *interaction* - the same agents,
the same game, the same emergent outcome."

## Evidence map

- Map each theorem, mechanism property, convergence claim, and empirical interaction claim to a
  verifiable location in the paper, appendix, supplement, or artifact.
- For theory, state the game, the information structure, the solution concept, assumptions,
  proof dependencies, and failure modes clearly enough for a game theorist.
- For experiments, report the environment, number of agents, opponent/population set, training
  regime, evaluation opponents, metrics, hyperparameter ranges, chosen settings, seeds,
  repeated runs, compute, and runtime.
- For small or noisy strategic differences, add uncertainty: standard errors, confidence
  intervals, or paired tests over seeds and over opponents.
- Explain any missing code or environment honestly, and describe how a reader could reproduce
  the interaction in principle.
- Keep the artifact consistent with the paper; a claim the artifact cannot demonstrate is a
  review-risk multiplier.

## Claim-to-evidence audit table

| Claim | Pure-theory answer | Learning-plus-game answer |
|---|---|---|
| Solution concept reached | Proof with the game and information structure stated | Plus convergence curves under other agents' adaptation |
| Opponents / population | NA if fully analytical | The exact opponent set and how it was chosen |
| Seeds and variance | NA for deterministic results | Required for every stochastic curve and payoff table |
| Compute | NA | Hardware, per-run time, and total number of self-play runs |

The rest: https://github.com/brycewang-stanford/Awesome-Journal-Skills/blob/HEAD/AAMAS-Skills/skills/aamas-reproducibility/SKILL.md