icml-topic-selection
Use when deciding whether a manuscript fits ICML, choosing the main research track versus the ICML Position Papers track or another venue (NeurIPS, ICLR, AISTATS, UAI, COLT, MLSys, TMLR, JMLR), or rerouting an ML paper based on its contribution type, strength of evidence, theory-versus-empirical balance, and interest to the broad ICML machine-learning community. Use before committing effort to an ICML submission.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icml-topic-selection --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.
# ICML Topic Selection Use this before committing to ICML. ICML rewards original, rigorous machine-learning research of significant interest to the ML community. It is not the best route for every AI application or position argument. ## Strong fit - A core ML method, theory, optimization, probabilistic model, RL algorithm, evaluation method, systems contribution, or trustworthy-ML result. - A use-inspired paper where the ML technique, evaluation, or insight is itself important to the ML community. - A theory paper with clear assumptions and meaningful implications. - An empirical study that improves how ML is evaluated, reproduced, scaled, or understood. - A paper that can show soundness, originality, significance, clarity, and reproducibility within ICML's format. ## Weak fit - A domain deployment with little ML novelty. - A benchmark win without mechanism or fair baselines. - A position or argument paper better suited to the ICML Position Papers track. - A replication, survey, dataset report, or engineering system better matched to another venue. - A paper that needs more than appendices or supplement to make the main contribution intelligible. ## Routing decisions - Main-track I
- Strong fit
- Weak fit
- Routing decisions
- Fit-versus-reroute table
- Worked vignette: where does the optimizer paper go
- Output format
What does the icml-topic-selection skill do?
Use when deciding whether a manuscript fits ICML, choosing the main research track versus the ICML Position Papers track or another venue (NeurIPS, ICLR, AISTATS, UAI, COLT, MLSys, TMLR, JMLR), or rerouting an ML paper based on its contribution type, strength of evidence, theory-versus-empirical balance, and interest to the broad ICML machine-learning community. Use before committing effort to an ICML submission.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icml-topic-selection --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.