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Ccs Author Response

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brycewang-stanford
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.

Overview

Publisherbrycewang-stanford
RepositoryAwesome-Journal-Skills
Skill nameccs-author-response
Stars
1.2K
Forks
159
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by brycewang-stanford on GitHub. Read the source before you install it.

Installation

Install the Ccs Author Response AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ACM-CCS-Skills/skills/ccs-author-response
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git /tmp/Awesome-Journal-Skills
mkdir -p .claude/skills
cp -r /tmp/Awesome-Journal-Skills/ACM-CCS-Skills/skills/ccs-author-response .claude/skills/ccs-author-response
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ccs Author Response in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ccs Author Response on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ccs Author Response is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

text
[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>

Frequently asked questions

What does the Ccs Author Response AI skill do?

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.

Why use Ccs Author Response on TypingMind?

Because you install it once and use it with any model. Ccs Author Response is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ccs Author Response in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ACM-CCS-Skills/skills/ccs-author-response. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ccs Author Response?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ccs Author Response?

As many as you like. As long as a model supports skills, you can use Ccs Author Response with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ccs Author Response AI skill free?

Yes. It is published on GitHub by brycewang-stanford under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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