iclr-topic-selection
Use when deciding whether a project is a strong ICLR submission, should be reframed for ICLR, or should be routed to NeurIPS, ICML, AAAI, AISTATS, ACL, CVPR, KDD, or another venue. Use when a project lacks a clear representation-learning insight, when an application result needs a learning contribution to fit ICLR, or when weighing ICLR's deep-learning center of gravity against a better-matched venue.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill iclr-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.
# ICLR Topic Selection Use this when a project is still movable. ICLR is broad, but the paper should teach the learning community something about representations, objectives, models, data, optimization, evaluation, or deployment. ## Strong ICLR signals - A clear representation-learning, model-behavior, optimization, generative modeling, RL, theory, or evaluation contribution. - Evidence that changes how researchers should build, analyze, or judge learning systems. - A simple central claim that can be verified by focused theory, experiments, or artifacts. - Interest beyond one dataset, product, or application vertical. - Honest limitations and ethics treatment for high-impact model or data claims. ## Weak ICLR signals - Pure application paper with little learning insight. - Incremental benchmark bump without mechanism, analysis, or robust evidence. - Closed system claim that reviewers cannot inspect or reproduce. - Dataset-only paper without a learning-representation or evaluation advance. - Theory result disconnected from modern learning practice and not routed to a theory-focused venue. ## Routing logic - Prefer NeurIPS or ICML for broader ML method/theory work with less ICLR-spec
- Strong ICLR signals
- Weak ICLR signals
- Routing logic
- Fit-versus-route decision table
- Worked vignette
- Reviewer-pushback patterns
- Output format
What does the iclr-topic-selection skill do?
Use when deciding whether a project is a strong ICLR submission, should be reframed for ICLR, or should be routed to NeurIPS, ICML, AAAI, AISTATS, ACL, CVPR, KDD, or another venue. Use when a project lacks a clear representation-learning insight, when an application result needs a learning contribution to fit ICLR, or when weighing ICLR's deep-learning center of gravity against a better-matched venue.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill iclr-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.