franklee16/academic-research-skills/aaai-artifact-evaluation

Claude Code and agent skill by franklee16/academic-research-skills

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Checking that the SKILL.md file is still in the repository.

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.

Install

npx vskill@latest install franklee16/academic-research-skills/aaai-artifact-evaluation

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.

Output format

text
[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>

Security

  • Security scanPassed, 95 of 100Pattern checks on the skill's files.
  • Model reviewPassed

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

Popularity

Repository stars
219
Repository forks
33
7-day trend score
27

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