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aaai-artifact-evaluation

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.

文档与办公1.2kAAAI-Skills/skills/aaai-artifact-evaluation/SKILL.md

安装

把这段话发给 Claude Code、Codex 或 Cursor。智能体会先检查安全性,你确认后才安装。

读取 https://funcoding.ai/skills/brycewang-stanford/awesome-journal-skills/aaai-artifact-evaluation/install.md ,按里面的步骤帮我安装这个 Skill。

SKILL.md

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.

Reviewer actionPassesFails
Opens the ZIPsane tree, top READMEnested archives, 0-byte files
Reads appendixmaps to numbered claimscontradicts the paper
Tries one commandreproduces one headline numberneeds private data or credentials
Scans for identitynothing reveals authorsGit 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>

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