본문으로 건너뛰기
cccskills
무료GitHub에 공개

ccs-review-process

Use when explaining or planning around ACM CCS peer review, the two-cycle HotCRP workflow, decision categories including minor revision, the rebuttal window, the adversarial reviewer pool, ethics and disclosure scrutiny, meta-review dynamics, and ACM proceedings outcomes.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

실행 점수 미검증

설치 방법 보기

포함된 파일(1)

  • SKILL.md3.4 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

CCS Review Process

Use this to reason about review-stage strategy. Reopen the current CFP, the cycle's HotCRP site, the reviewer guidelines if posted, and the ethics policy before making process claims.

Process model

  • CCS runs two review cycles per year on HotCRP, each with abstract registration, full-paper submission, review, a rebuttal window, and notification.
  • Reviewers assess threat-model soundness, novelty, technical correctness, evidence quality, ethics and responsible disclosure, and significance to the security community.
  • Decisions are not binary: CCS 2026 used accept, minor revision (authors revise for the same cycle under a shepherd or check), and reject. A first-cycle reject cannot return that year.
  • An ethics or disclosure concern can sink an otherwise strong paper; the ethics reviewer's view carries weight independent of technical scores.
  • Accepted papers publish in the ACM Digital Library proceedings, so camera-ready and metadata compliance matter as much as the initial decision.

Who reviews here

  • The pool is adversarial by training: expect at least one reviewer to attack your threat model, look for the assumption that makes the attack easy, and probe whether the defense was tested against an adaptive attacker.
  • CCS is broad, so a paper may draw reviewers from an adjacent sub-area; the threat model and intro must orient a web-security reviewer to a crypto result and vice versa.
  • Borderline papers usually fall on one of three edges: an attacker assumption too strong to be interesting, a defense never adapted against, or an ethics gap left unaddressed.

Scoring leverage table

Review dimensionWhat raises itWhat sinks it
Threat modelA tight, realistic adversary stated up frontCapabilities that quietly grow to make the attack work
NoveltyAn attack class or guarantee the literature lackedAn increment over a cited paper with no new idea
EvidenceEnd-to-end demonstration against a real targetLab-only results against a strawman
EthicsDisclosure done, harm minimized, reasoned in the paperReal-world harm with no disclosure or IRB reasoning

Stage-by-stage realism

  • Initial reviews: triage by what the meta-reviewer will weigh — threat model and evidence — not by reviewer tone.
  • Rebuttal: the window is short and structured; one crisp answer to the decision-critical objection beats an exhaustive point-by-point.
  • Decision: the meta-review synthesizes; one unresolved threat-model or ethics objection outweighs several resolved clarity complaints.
  • Minor revision: treat it as conditional acceptance with a checklist, not a debate; do exactly what the shepherd asks within the cycle's timeline.

Output format

[Current stage] submitted / reviews / rebuttal / decision / revision / camera-ready
[Decision actors] <reviewers / ethics reviewer / meta-reviewer / chairs>
[Likely leverage] <threat model / novelty / evidence / ethics>
[Forbidden moves] <identity leak / new unsupported results / ignoring the shepherd>
[Next response move] <one action>

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

Use when packaging AAAI code, data, multimedia appendices, technical appendices, reproducibility evidence, and post-acceptance artifact releases without violating double-blind or immutable-supplement rules.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

brycewang-stanford/Awesome-Journal-Skills1.3천2026년 9월 27일 업데이트

Use when drafting an AAAI author response (rebuttal) under the single short character-limited author-feedback window, the no-URL rule, no-new-results guidance, AI-generated-review handling, and the AAAI two-phase review process where Phase-2 papers receive one feedback round before SPC/AC discussion.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

brycewang-stanford/Awesome-Journal-Skills1.3천2026년 9월 27일 업데이트

Use when preparing an accepted AAAI paper for camera-ready source submission to AAAI Press, including proceedings page limits, two-column template compliance, copyright transfer, purchased extra technical pages, deanonymization, registration, oral or poster presentation, and final public artifact release.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

brycewang-stanford/Awesome-Journal-Skills1.3천2026년 9월 27일 업데이트

Use when designing or auditing AAAI experiments for the broad-AI program committee, including baselines, ablations, statistical significance, robustness, human evaluation, AI-for-Social-Impact and alignment/safety evidence, compute and cost reporting, and reproducibility-checklist alignment for Phase-1 survival.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

brycewang-stanford/Awesome-Journal-Skills1.3천2026년 9월 27일 업데이트

Use when positioning an AAAI paper's novelty against archival work, contemporaneous arXiv or workshop papers, and AAAI/IJCAI/NeurIPS/ICML/ICLR neighbors across the broad AI scope, while staying inside AAAI's dual-submission and AI-as-source policy constraints and writing a related-work section legible to non-specialist reviewers.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

brycewang-stanford/Awesome-Journal-Skills1.3천2026년 9월 27일 업데이트

Use when strengthening an AAAI paper's reproducibility checklist (placed after references), experimental traceability, seed and hyperparameter reporting, compute and cost disclosure, dataset access and licensing, code/data ZIP readiness, and the claim-to-evidence map that Phase-1 reviewers use to judge rigor across AAAI's broad AI scope.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

brycewang-stanford/Awesome-Journal-Skills1.3천2026년 9월 27일 업데이트

brycewang-stanford의 스킬 모두 보기

이 스킬의 문제 신고