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Ccs Review Process

CommunityPopular
brycewang-stanford
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.

Overview

Publisherbrycewang-stanford
RepositoryAwesome-Journal-Skills
Skill nameccs-review-process
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 Review Process 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-review-process
  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-review-process .claude/skills/ccs-review-process
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ccs Review Process 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 Review Process 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 Review Process 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 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

text
[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>

Frequently asked questions

What does the Ccs Review Process AI skill do?

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.

Why use Ccs Review Process on TypingMind?

Because you install it once and use it with any model. Ccs Review Process 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 Review Process 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-review-process. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ccs Review Process?

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 Review Process?

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

Is the Ccs Review Process 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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