ijcai-artifact-evaluation
Use when packaging IJCAI or IJCAI-ECAI code, data, proofs, models, and appendices as reproducibility evidence or supplementary material, especially when there is no separate artifact-evaluation badge but reviewers need convincing, anonymous, deadline-safe evidence.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ijcai-artifact-evaluation --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# IJCAI Artifact Evaluation Use this for artifact packaging around IJCAI. Treat the current reproducibility guidelines and supplementary-material rules as controlling; do not assume a separate formal artifact evaluation track unless the current cycle announces one. ## Package design - Decide what reviewers need to classify the results as convincing or credible: proofs, pseudocode, datasets, code, model cards, logs, environment details, or ablation notebooks. - Keep essential evidence in the paper whenever space allows because reviewers are not required to read supplementary material. - Put optional evidence in the Technical Appendix or ZIP, respecting the current size and format limit. IJCAI-ECAI 2026 allowed up to 50MB in PDF or ZIP form. - Anonymize repository paths, user names, institutions, license headers, model checkpoints, data provenance, and notebook metadata. - Include a minimal run map: environment, dependencies, hardware, commands, expected outputs, runtime, seeds, and known limitations. - For proprietary or restricted data/code, explain why it cannot be shared and provide enough detail for in-principle reproduction. ## Evidence by claim type IJCAI usually has no separa
- Package design
- Evidence by claim type
- Worked vignette: packaging a SAT-solver paper
- Reviewer pushback and the venue-specific fix
- Output format
What does the ijcai-artifact-evaluation skill do?
Use when packaging IJCAI or IJCAI-ECAI code, data, proofs, models, and appendices as reproducibility evidence or supplementary material, especially when there is no separate artifact-evaluation badge but reviewers need convincing, anonymous, deadline-safe evidence.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ijcai-artifact-evaluation --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.