본문으로 건너뛰기
cccskills
무료GitHub에 공개

ccs-author-response

Use when drafting an ACM CCS rebuttal during the HotCRP author-response window, covering threat-model defenses, adaptive-attack objections, ethics and disclosure questions, anonymity constraints, adversarial reviewer pushback patterns, and decision-focused replies aimed at the meta-reviewer.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

설치 방법 보기

포함된 파일(1)

  • SKILL.md3.7 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

CCS Author Response

Use this after CCS reviews are released. Reopen the current HotCRP rebuttal instructions and any length or format limits before drafting, because response mechanics are set per cycle.

Triage

  • Answer concerns that affect the decision: threat-model soundness, novelty, correctness, evidence quality, ethics, and disclosure. Skip cosmetic complaints unless space allows.
  • Use evidence already in the submission: sections, appendices, measurement tables, and the ethics section. The rebuttal clarifies the submitted paper; it does not add a new paper.
  • Keep the reply anonymous. Do not reveal institution, authorship, private URLs, or the identity of a disclosed vendor if that would deanonymize you.
  • Correct factual misreadings first, then address the strongest objection to the attacker model or the adaptive evaluation.
  • Do not promise experiments you cannot run before camera-ready; promise only edits that add no unsupported claim.

Drafting pattern

  1. State the decision-critical correction or concession in the first line.
  2. Point to exact submitted evidence (section, figure, appendix).
  3. Explain the security consequence — why the attack still holds, or the defense still binds.
  4. Offer a scoped camera-ready edit only where it clarifies without over-claiming.

Adversarial reviewer pushback patterns

PushbackWhat it signalsCCS-ready fix
"The attacker assumes too much"The threat model looks unrealisticPoint to where the capability is justified, or scope the claim to the realistic subset and concede the rest
"You never tested an adaptive attacker"The defense evaluation looks incompleteReference the adaptive experiment if present; if absent, concede and commit to a bounded camera-ready addition only if honestly feasible
"This is already a known CVE"A prior-art gapDistinguish root cause, scope, or impact from the CVE, with the specific technical delta
"The ethics of this are unaddressed"Disclosure or harm concernState the disclosure timeline, the harm-minimization steps, and the reasoning, quoting the ethics section

Response micro-example

Reviewer objection: the key-recovery attack assumes co-located measurement that real attackers cannot achieve. Reply skeleton:

  1. Concede the strongest form needs co-location; scope the practical claim to that setting.
  2. Point to Section 6 where a remote variant with degraded rate is already measured.
  3. Note the defense in Section 7 blocks both variants, so the contribution stands.
  4. Offer one camera-ready sentence making the co-location assumption explicit in the intro.

Rebuttal calibration

  • One decision-critical point per reviewer beats an exhaustive rebuttal; CCS meta-reviewers read for whether the central threat-model or ethics objection was actually resolved.
  • Never paste new experimental results the reviewers cannot verify; describe what exists and anchor it in the submission.
  • Answer the ethics reviewer directly and early; an unaddressed disclosure concern outweighs resolved technical nits at decision time.

Output format

[Priority issue] <reviewer concern>
[Decision dimension] threat model / novelty / correctness / evidence / ethics
[Draft response] <CCS-ready anonymous text>
[Evidence anchor] <section / figure / appendix item>
[Forbidden content removed] <identity leaks, unverifiable new results, over-promises>

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