claude

iclr-skills

Twelve ICLR-specific skills for ML conference submission strategy, grounded in official ICLR 2026 CFP, Author Guide, Reviewer Guide, Code of Ethics, Code of Conduct, and LLM-use policy.

1,413 on GitHubby Bryce Wangv1.1.0MITupdated 1 month agoacademic
/

Plugin iclr-skills from marketplace awesome-journal-skills in brycewang-stanford/Awesome-Journal-Skills. Commands read from the repo's manifest — 1 month ago.

What it installs

Skills (12)

  • iclr-artifact-evaluation

    Use when packaging ICLR code, data, checkpoints, demos, logs, and reproduction instructions for reviewers or post-acceptance release, including anonymized links and private OpenReview discussion-period sharing. Use when a reviewer asks for a missing repro path, when a public comment questions whethe

  • iclr-author-response

    Use when drafting ICLR OpenReview author discussion replies, revision notes, public comments, restricted comments, and responses to reviewer or AC concerns during the discussion period. Use when reviews land and you must decide what to answer publicly versus privately, how to log a discussion-period

  • iclr-camera-ready

    Use when preparing an accepted ICLR paper for camera-ready submission, OpenReview metadata, poster instructions, slide deck, project page, video, and publication logistics. Use when de-anonymizing the final PDF, folding in promised discussion-period fixes, confirming the camera-ready page allowance

  • 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

  • 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 w

  • iclr-reproducibility

    Use when strengthening reproducibility for ICLR papers, including seeds, variance, compute, datasets, implementation details, ethics statements, and reviewer-verifiable evidence. Use when writing the ICLR reproducibility statement, when a reviewer says a result is not verifiable, or when mapping eac

  • iclr-review-process

    Use when explaining or planning around the ICLR review process, including OpenReview public review, AC/SAC roles, reviewer questions, discussion, revisions, final recommendations, and ethics escalation. Use when deciding whether to reply publicly or privately, how a permanently visible review thread

  • iclr-submission

    Use when auditing an ICLR main-conference submission for OpenReview readiness, double-blind anonymity, page limits, reciprocal reviewing, LLM-use disclosure, dual-submission policy, supplementary material, and desk-reject risk.

  • iclr-supplementary

    Use when organizing ICLR appendices, supplementary files, anonymous code/data, revised PDFs, private links, and discussion-period updates under OpenReview rules. Use when deciding what stays in the main text versus the appendix, how to label a discussion-period revision so reviewers find changes fas

  • 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 contribut

  • iclr-workflow

    Use when planning an ICLR project timeline from topic selection through OpenReview submission, discussion, revision, decision, camera-ready, poster, video, and public artifact release. Use when sequencing milestones against the current cycle's OpenReview deadlines, budgeting time for the long public

  • iclr-writing-style

    Use when revising an ICLR manuscript for learning-representation framing, OpenReview readability, contribution clarity, limitations, ethics, and reviewer navigation. Use when the core representation insight is buried, when an abstract must read well as an OpenReview snippet, or when adding a "what t

View source on GitHub copaper.ai

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