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Evaluate whether a research paper fits NeurIPS, ICLR, ICML, MLSys, ASPLOS, CVPR, or adjacent venues based on contribution type, evidence, audience, novelty, experiment norms, and reviewer expectations.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Prepare artifact-evaluation submissions for ML and systems conferences, including artifact scope, installability, reproducibility claims, hardware needs, badges, expected runtime, and reviewer instructions.
Audit experiments for top ML, CV, systems, and architecture submissions, checking baselines, ablations, metrics, statistical strength, datasets, stress tests, failure cases, and claim coverage.
Build a top-conference paper strategy from raw results, including central claim, contribution framing, section outline, reviewer value proposition, and claim-evidence alignment.
Plan a top-conference rebuttal or author response by clustering reviewer comments, identifying misunderstandings, concessions, evidence, response priority, and camera-ready promises.
Simulate critical top-conference reviewers for a draft, producing likely strengths, weaknesses, score drivers, clarity objections, missing experiments, novelty concerns, and rebuttal-preparation notes.
Build and audit conference submission checklists for paper PDF, supplement, OpenReview/CMT metadata, anonymity, ethics, broader impact, reproducibility, author forms, and deadline readiness.
| name | venue-fit |
| description | Evaluate whether a research paper fits NeurIPS, ICLR, ICML, MLSys, ASPLOS, CVPR, or adjacent venues based on contribution type, evidence, audience, novelty, experiment norms, and reviewer expectations. |
| version | 0.1.0 |
Use this skill before committing to a target conference.
Return Venue-fit table, Best target, Backup targets, Fit risks, and
Required paper changes.
