iclr-related-work
Use when positioning an ICLR paper against prior work, concurrent OpenReview submissions, arXiv papers, benchmark lineages, and adjacent learning-representation claims. Use when a reviewer cites a paper you missed, when a public comment disputes your novelty, or when separating "shares a component with" from "solves the same representation-learning problem" so the claim survives permanent public scrutiny.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill iclr-related-work --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 Related Work Use this to make the novelty claim robust under ICLR review. ICLR reviewers often know recent OpenReview, arXiv, and workshop work, so the related-work strategy must survive public comparison. ## Positioning checks - Identify the closest prior method, theory result, dataset, benchmark, or analysis paper. - Separate "uses a similar component" from "solves the same scientific pro
What does the iclr-related-work skill do?
Use when positioning an ICLR paper against prior work, concurrent OpenReview submissions, arXiv papers, benchmark lineages, and adjacent learning-representation claims. Use when a reviewer cites a paper you missed, when a public comment disputes your novelty, or when separating "shares a component with" from "solves the same representation-learning problem" so the claim survives permanent public scrutiny.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill iclr-related-work --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.