neurips-experiments
Use when stress-testing NeurIPS experimental evidence, including baselines, ablations, data splits, compute, negative results, real-world use, and claim-to-evidence calibration.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill neurips-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.
# NeurIPS Experiments Use this skill before submission or rebuttal when the main question is whether the evidence supports the NeurIPS claim. It is not enough to win a leaderboard; reviewers need to know why the result is scientifically meaningful. ## Experiment audit - Baselines: include strong, current, tuned baselines and explain any missing comparison. - Ablations: isolate the mechanism, not just remove components at random. - Robustness: test across seeds, datasets, distribution shifts, scales, hyperparameters, or realistic deployment conditions when relevant. - Compute: disclose hardware, training time, resource assumptions, and whether comparisons are fair. - Data: document splits, contamination controls, license, demographic or domain coverage, and privacy/consent limits. - Negative results: use them to calibrate claims; NeurIPS has a contribution type for negative results, but the bar remains high. - Use-inspired work: connect results to the real task without turning the paper into an application report with no ML contribution. ## Claim-to-evidence ladder NeurIPS reviewers read experiments against the claim type. Put every headline claim on the ladder before deciding wheth
- Experiment audit
- Claim-to-evidence ladder
- Baseline fairness table
- Review-dimension stress test
- Rebuttal triage gate
- Rebuttal-ready evidence
- Output format
What does the neurips-experiments skill do?
Use when stress-testing NeurIPS experimental evidence, including baselines, ablations, data splits, compute, negative results, real-world use, and claim-to-evidence calibration.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill neurips-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.