ijcai-experiments
Use when designing or auditing IJCAI or IJCAI-ECAI experiments, baselines, ablations, statistical evidence, hyperparameter reporting, compute descriptions, dataset handling, ethics risks, and reproducibility evidence for AI papers.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ijcai-experiments --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 Experiments Use this before submission when the experimental story is not yet locked. IJCAI reviewers can score novelty, correctness, clarity, significance, impact, presentation, ethics, and reproducibility. ## Experiment audit - Map each major claim to a table, figure, theorem, ablation, proof, or qualitative analysis. - Include strong, current, and properly tuned baselines; explain any missing baseline before reviewers ask. - Report dataset splits, preprocessing, metrics, search ranges, final hyperparameters, selection criteria, random seeds or repeats, and compute infrastructure. - Add ablations for the core mechanism, not just peripheral architecture choices. - Use uncertainty estimates, paired tests, confidence intervals, or repeated runs when small differences could change the conclusion. - For sensitive data or human-facing systems, document privacy, consent, copyright, safety, fairness, misuse, and deployment limits. - Keep enough details in the main paper for credible reproduction even if reviewers ignore the supplementary material. ## What IJCAI reviewers score the evidence on IJCAI draws reviewers from symbolic AI, search, planning, constraint satisfaction, KR, m
- Experiment audit
- What IJCAI reviewers score the evidence on
- Worked vignette: a heuristic-search paper
- Reviewer pushback and the venue-specific fix
- Output format
What does the ijcai-experiments skill do?
Use when designing or auditing IJCAI or IJCAI-ECAI experiments, baselines, ablations, statistical evidence, hyperparameter reporting, compute descriptions, dataset handling, ethics risks, and reproducibility evidence for AI papers.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ijcai-experiments --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.