ijcai-reproducibility
Use when strengthening IJCAI or IJCAI-ECAI reproducibility evidence for theory, algorithms, datasets, and computational experiments under the official convincing/credible/irreproducible reviewer rubric and optional reproducibility-section guidance.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ijcai-reproducibility --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 Reproducibility Use this before submission and again before camera-ready. Reopen the current reproducibility page; IJCAI's rubric and checklist can change. ## Evidence map - Target at least a credible reproducibility rating for every major result; aim for convincing when resources can be shared safely. - For new algorithms, include a conceptual outline or pseudocode in the paper. - For theory, state assumptions, formal claims, citations to tools, proof sketches, and proofs for novel claims. - For datasets, cite existing sources, describe unavailable or proprietary datasets, and include or promise release of new datasets only when legally possible. - For computational experiments, report final hyperparameters, search ranges, selection criteria, seeds or repeat strategy, software versions, compute infrastructure, and runtime. - Add an optional "Reproducibility" section when it resolves likely reviewer doubts, but do not depend on supplementary material for central claims. ## Rubric mapping by contribution shape 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 t
- Evidence map
- Rubric mapping by contribution shape
- Worked vignette: a multi-agent coordination paper
- Reviewer pushback and the venue-specific fix
- Output format
What does the ijcai-reproducibility skill do?
Use when strengthening IJCAI or IJCAI-ECAI reproducibility evidence for theory, algorithms, datasets, and computational experiments under the official convincing/credible/irreproducible reviewer rubric and optional reproducibility-section guidance.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ijcai-reproducibility --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 984 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.