iclr-experiments
Use when designing or auditing ICLR experiments, including baselines, ablations, scaling laws, robustness, statistics, benchmarks, human evaluation, and compute reporting. Use when a reviewer questions whether a representation-learning or model gain is real, when you must isolate one mechanism with an ablation, or when preparing a small compute-matched control that can be posted inline during the public discussion period.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill iclr-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.
# ICLR Experiments Use this before submission or during a revision pass to stress-test empirical claims. ICLR experiments should answer the scientific question, not merely assemble a leaderboard. ## Experiment audit - Match each experiment to a claim in the introduction. - Compare against current strong baselines, open-source systems, and the most relevant recent OpenReview/arXiv papers. - Add ablations that isolate one mechanism at a time. - Report variance across seeds or runs when randomness can change conclusions. - Include robustness checks for dataset shift, prompt changes, architecture variants, hyperparameter sensitivity, or compute scale when those affect the claim. - State compute budget, hardware, training time, inference cost, and environmental or access limits where relevant. - For human evaluation, document task, annotator instructions, aggregation, quality control, and IRB or ethics status when needed. ## Reviewer questions to pre-answer - Is the baseline tuned fairly? - Does the method win because of more compute, data, parameters, or prompt search? - Does the effect persist outside the easiest benchmark? - Are negative results hidden? - Can a reviewer reproduce the
- Experiment audit
- Reviewer questions to pre-answer
- What ICLR reviewers reward in evidence
- Worked vignette
- Reviewer-pushback patterns
- Output format
What does the iclr-experiments skill do?
Use when designing or auditing ICLR experiments, including baselines, ablations, scaling laws, robustness, statistics, benchmarks, human evaluation, and compute reporting. Use when a reviewer questions whether a representation-learning or model gain is real, when you must isolate one mechanism with an ablation, or when preparing a small compute-matched control that can be posted inline during the public discussion period.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill iclr-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.