Skills

Aaai Artifact Evaluation

Use when packaging AAAI code, data, multimedia appendices, technical appendices...

Install

npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aaai-artifact-evaluation -g -a claude-code -y
Category
Engineering
Updated
1mo ago
License
MIT
Context cost
Lightabout 836 tokens
Runs scripts
No, instructions only

What it does

Use when packaging AAAI code, data, multimedia appendices, technical appendices, reproducibility evidence, and post-acceptance artifact releases without violating double-blind or immutable-supplement rules.

AAAI Artifact Evaluation

Use this to prepare artifacts that reviewers can use to assess reproducibility. AAAI supplementary material is part of the submission record; after review starts, do not assume it can be updated.

Artifact package

  • Provide a technical appendix for proofs, algorithms, assumptions, hyperparameters, and extended experiments.
  • Provide code/data ZIPs that reproduce main tables or figures, with a short README, environment, commands, seeds, expected outputs, and runtime.
  • Provide multimedia appendices only when they support the technical claim.
  • Remove author names, usernames, paths, repository history, cloud buckets, API keys, and metadata.
  • Avoid web pointers in the reviewed submission unless current rules explicitly allow them.
  • Include licensing and access notes for datasets, models, and third-party code.

AAAI-specific discipline

  • Treat the supplementary deadline as final.
  • Verify ZIP integrity before submission; missing or corrupted files may not be fixable during rebuttal.
  • Make the reproducibility checklist consistent with the artifact package.
  • Prepare a post-acceptance public release path but keep review artifacts anonymous.

What an AAAI reviewer actually opens

AAAI does not run a separate badged artifact-evaluation committee the way some systems venues do; the same broad-AI reviewer who scores the paper also inspects whatever supplement you attach. That reviewer may be a planning, knowledge-representation, or constraint-satisfaction specialist rather than a deep-learning engineer, so the artifact has to be legible without insider tooling. Optimize for a reviewer who skims, not one who will spend an afternoon configuring a cluster.

Read the whole SKILL.md on GitHub

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