AAAI Artifact Evaluation
SkillDev toolsThis skill guides your AI through packaging the materials that go with an AAAI paper, such as code, data, appendices, and reproducibility evidence. Once added, your AI can prepare these artifacts for peer review and for release after your paper is accepted, while staying within double-blind and immutable-supplement rules.
Available today. Use it from your connected AI after setup.
No other account needed.
Add the skill and tell your AI which paper materials you need to package. It will walk you through preparing them for review or for post-acceptance release.
Then ask your AI: use the AAAI Artifact Evaluation skill
What your AI can do with it
- Package code, data, and appendices for AAAI review
- Prepare multimedia and technical appendices
- Assemble reproducibility evidence for your paper
- Prepare artifact releases after acceptance
- Keep packaging within double-blind and immutable-supplement rules
What this skill tells your AI
The instructions your AI receives, as published by brycewang-stanford/awesome-journal-skills in AAAI-Skills/skills/aaai-artifact-evaluation/SKILL.md and read by ahel’s review.
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.
| Reviewer action | Passes | Fails |
|---|---|---|
| Opens the ZIP | sane tree, top README | nested archives, 0-byte files |
| Reads appendix | maps to numbered claims | contradicts the paper |
| Tries one command | reproduces one headline number | needs private data or credentials |
| Scans for identity | nothing reveals authors | Git logs or home paths leak |
Phase-1 artifact red flags
Because clearly-below-bar papers can be cut before author feedback, a supplement that looks thin or unrunnable is a cheap reason to summary-reject. Avoid these:
- Checklist promises released code, but the ZIP only holds figures and no scripts.
- A "see our repository" pointer to a mutable, deanonymizing URL.
- Multimedia attached for spectacle that carries no technical claim, inflating size with no rigor.
- Datasets shipped with no license note, leaving reuse legality unverifiable.
Worked vignette
A constraint-solving paper claims a 30% node-expansion reduction. The team ships a large ZIP of raw
solver logs but no driver script. The reproduction path is empty, so artifact status is "risky"; the
fix is a small run_main.py that regenerates Table 2 from seeds, a trimmed log sample, and a license
for the benchmark instances. The raw dump moves to the post-acceptance release.
Output format
[Artifact status] complete / partial / risky / unavailable
[Submitted files] technical appendix / multimedia appendix / code-data ZIP
[Reviewer reproduction path] <commands and expected output>
[Anonymity risks] <metadata, links, paths, logs>
[Missing items] <data, code, seeds, licenses, hardware>
Signals
- GitHub stars
- 1k
- Forks
- 146
- Last commit
- Aug 2026
Advanced
- Catalog kind
- skill
- Gateway key
aaai-artifact-evaluation- Source
- github.com/brycewang-stanford/awesome-journal-skills