
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- Repository
- brycewang-stanford/Awesome-Journal-Skills1.3k
- 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.
