From ijcai-skills
Strengthens IJCAI/ECAI reproducibility evidence using the official reviewer rubric. Maps contributions (algorithms, theory, datasets, experiments) to credible/convincing ratings and guides evidence drafting.
How this skill is triggered — by the user, by Claude, or both
Slash command
/ijcai-skills:ijcai-reproducibilityThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Use this before submission and again before camera-ready. Reopen the current reproducibility
Use this before submission and again before camera-ready. Reopen the current reproducibility page; IJCAI's rubric and checklist can change.
IJCAI's rating moves from irreproducible to credible to convincing. Because the PC spans symbolic, search, planning, KR, multi-agent, and learning work, the evidence that earns "convincing" differs by contribution. Map each result to the right column before drafting.
| Contribution | Minimum for "credible" | What lifts it to "convincing" |
|---|---|---|
| New algorithm (search/planning/CSP) | Pseudocode plus complexity claim in body | Runnable code, instance generator, seeds, version pins |
| Theoretical result | Assumptions and proof sketch in body | Full appendix proofs, citations to formal tools |
| Multi-agent / game-theoretic | Protocol, agent counts, payoffs | Released simulator, opponent policies, seeds |
| Dataset contribution | Source, licensing, collection described | Public release or controlled-access path, datasheet |
| Learning experiment | Hyperparameters and splits in body | Code, environment file, compute, repeat strategy |
A coordination-protocol paper claims faster convergence but reports no seeds and ships no simulator. To reach "convincing": state the agent count sweep and payoff matrix in the body; appendix the convergence proof sketch; release an anonymized simulator with fixed seeds and the opponent policies; pin library versions. Without the simulator the result stays "credible" at best, which an IJCAI reviewer may flag as a significance discount.
[Result inventory] <claim -> evidence location>
[Rubric target] convincing / credible / currently weak
[Missing details] <algorithm/theory/data/compute/hyperparameters/seeds>
[Paper fixes] <must be in main PDF>
[Supplement fixes] <optional or supporting evidence>
npx claudepluginhub brycewang-stanford/awesome-journal-skills --plugin ijcai-skillsAudits IJCAI/IJCAI-ECAI experiments for baselines, ablations, statistical evidence, hyperparameters, compute, dataset handling, ethics, and reproducibility.
Strengthens ICML reproducibility evidence: code/data availability, random seeds, compute disclosure, appendix evidence, and reviewer-facing claims.
Strengthens reproducibility for ICLR papers: maps claims to seeds, splits, commands, and compute; writes reproducibility statements and addresses reviewer concerns about verifiability.