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Ccs Camera Ready

CommunityPopular
brycewang-stanford
ccs-camera-ready

Use when preparing an accepted ACM CCS paper for the ACM Digital Library proceedings, covering de-anonymization, the ACM sigconf final format, ACM rights and eRights forms, badge placement from artifact evaluation, incorporation of shepherd and minor-revision requirements, disclosure-timing coordination, registration, and in-person presentation.

Overview

Publisherbrycewang-stanford
RepositoryAwesome-Journal-Skills
Skill nameccs-camera-ready
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 Camera Ready 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-camera-ready
  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-camera-ready .claude/skills/ccs-camera-ready
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ccs Camera Ready 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 Camera Ready 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 Camera Ready 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 Camera Ready

Use this after acceptance or after clearing a minor revision. Reopen the current CCS camera-ready instructions, the ACM rights form, the registration page, and the notification or shepherd email before advising authors.

Camera-ready audit

  • Convert the anonymous submission to the public proceedings version: author names, affiliations, acknowledgements, funding, and artifact links restored.
  • Apply the ACM sigconf final template and the ACM rights-management block; the DOI, the conference reference string, and the copyright notice come from the ACM eRights system.
  • Incorporate exactly what the shepherd or the minor-revision decision required, without expanding the contribution beyond what was reviewed.
  • Place any artifact-evaluation badges earned (Available, Functional, Reusable, Results Reproduced) as instructed; badge placement is part of the ACM camera-ready workflow.
  • Confirm the final title and abstract match the HotCRP-registered title and abstract if the cycle froze them at registration.
  • Coordinate disclosure timing: the public proceedings version and any released exploit or advisory should not appear before the agreed vendor-disclosure date.

Badge placement map

Badge earned in artifact evaluationWhere it goesWhat to confirm
Artifacts AvailableFirst page, with the archival DOI/URLThe archive is public and permanent before camera-ready
Artifacts Evaluated - FunctionalFirst pageThe committee's functional result is final
Artifacts Evaluated - ReusableFirst pageDocumentation matches the reusable claim
Results ReproducedFirst pageThe reproduced results match the camera-ready numbers

De-anonymization sweep

  • Restore the author block, acknowledgements, grants, and contribution statements the anonymous version stripped.
  • Rewrite third-person self-citations into natural first person where it improves clarity.
  • Replace anonymized repository placeholders with the public, licensed, archived artifact and test every link from a logged-out browser.
  • Re-check that de-anonymizing did not silently reveal an embargoed vendor before its patch.

ACM proceedings reflow

  • Switching from the submission build to the final sigconf build changes spacing; recheck every page break, protocol figure, and wide result table after adding the author block.
  • Material that fit the 12-page anonymous body can overflow once affiliations, acknowledgements, and the rights block are added; confirm the final length against the camera-ready page instruction rather than assuming.
  • Keep theorem, protocol, and section numbering identical to the reviewed version so the meta-review and shepherd record stay traceable.

Worked example: integrating a shepherd requirement

The shepherd asked that the adaptive-attack results move from the appendix into the main evaluation. Camera-ready move: promote the adaptive table into Section 6, add one sentence interpreting the degraded defense numbers honestly, and leave the full attack derivation in the appendix — without strengthening any claim the reviewers did not see.

Hedged logistics

  • Registration pricing, the exact camera-ready and rights-form deadlines, and badge-placement mechanics change every cycle; confirm against the notification email and current instructions.

Output format

text
[Camera-ready status] Ready / Needs fixes / Blocked
[Final package] <PDF / source / bib / ACM rights form / badges / artifacts>
[Policy checks] <format / authors / disclosure timing / registration / presentation>
[Reviewer-change map] <shepherd/revision requirement -> final edit>
[Remaining owner] <person -> task>

Frequently asked questions

What does the Ccs Camera Ready AI skill do?

Use when preparing an accepted ACM CCS paper for the ACM Digital Library proceedings, covering de-anonymization, the ACM sigconf final format, ACM rights and eRights forms, badge placement from artifact evaluation, incorporation of shepherd and minor-revision requirements, disclosure-timing coordination, registration, and in-person presentation.

Why use Ccs Camera Ready on TypingMind?

Because you install it once and use it with any model. Ccs Camera Ready 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 Camera Ready 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-camera-ready. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ccs Camera Ready?

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 Camera Ready?

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

Is the Ccs Camera Ready 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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