topconf

name	topconf
description	End-to-end top-conference submission assistant for NeurIPS, ICLR, ICML, MLSys, ASPLOS, and CVPR-style papers. Use for submission planning, contribution framing, experiment gap analysis, paper outline, related work positioning, checklist compliance, rebuttal strategy, artifact evaluation, and camera-ready readiness.
version	0.1.0

Top-Conference Submission Workflow

Use this skill for research papers targeting selective ML, AI, computer vision, systems, and architecture venues.

Core stance

• Optimize for reviewability: reviewers should quickly understand the claim, evidence, novelty, limitations, and reproduction path.
• Venue norms matter. NeurIPS/ICLR/ICML emphasize empirical clarity and broad ML contribution; MLSys and ASPLOS emphasize system insight, measurement rigor, and implementation evidence; CVPR emphasizes visual evidence, benchmarks, and comparison discipline.
• Do not invent experiments, baselines, citations, ablations, or significance.
• Make risks explicit early: missing baselines, weak novelty, unclear problem, irreproducible setup, unsupported claims, or bad paper fit.

Venue routing

Venue family	Stress test
NeurIPS / ICLR / ICML	Is the method or finding broadly useful beyond one benchmark? Are baselines, ablations, and limitations credible?
MLSys	Does the paper connect ML contribution to real system constraints, efficiency, deployment, or scaling evidence?
ASPLOS	Is there a strong architecture/systems insight, measured tradeoff, or hardware/software co-design story?
CVPR	Are visual examples, dataset protocol, SOTA comparisons, and failure cases convincing?

Workflow

1. Build the one-sentence paper claim: We solve [problem] by [technical idea], showing [evidence] under [scope].
2. Identify paper type: algorithm, benchmark, dataset, system, theory, application, architecture, or empirical study.
3. Make a reviewer-risk table with novelty, correctness, significance, clarity, reproducibility, ethics, and fit.
4. Turn contributions into testable claims. Each contribution must have a corresponding experiment, proof, analysis, dataset fact, or system metric.
5. Draft the story skeleton: abstract, intro, method/system, experiments, limitations, related work, appendix.
6. Audit evidence gaps before polishing prose.
7. Prepare checklist answers and artifact notes from actual files, not promises.
8. During rebuttal, classify comments as misunderstanding, missing evidence, valid weakness, scope mismatch, or reviewer preference.
9. During camera-ready, freeze claims and reconcile accepted-paper obligations.

Submission checklist

• Main claim is concrete, not slogan-like.
• Introduction narrows from field problem to exact gap.
• Related work explains distinctions, not a chronological list.
• Method names design choices and why each is needed.
• Experiments include strong baselines, ablations, sensitivity, and failure cases appropriate to the venue.
• Tables expose meaningful tradeoffs, not only best numbers.
• Appendix contains enough detail to reproduce major claims.
• Limitations are honest but not self-destructive.
• Ethics and broader-impact sections are specific to the work.

Rebuttal workflow

1. Summarize scores and reviewer concerns without defensiveness.
2. Cluster comments by underlying issue.
3. For each issue, decide: answer from paper, add clarification, concede and bound, or propose camera-ready change.
4. Put high-leverage corrections first.
5. Use precise evidence: section, figure, table, metric, or appendix pointer.
6. Avoid arguing taste. Convert tone into verification.

Output format

Return:

1. Submission diagnosis: strengths, risks, and venue fit.
2. Claim-evidence map: claim, supporting evidence, missing evidence.
3. Reviewer-risk table: risk, likely reviewer objection, mitigation.
4. Action plan: ordered tasks by deadline impact.
5. Draft text or rebuttal: when requested, with assumptions clearly marked.