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Ccs Topic Selection

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brycewang-stanford
ccs-topic-selection

Use when deciding whether a security project fits ACM CCS versus IEEE S&P, USENIX Security, NDSS, PETS, or a crypto/theory venue, identifying the security contribution type, and sharpening the threat model and attacker capability before writing begins.

Overview

Publisherbrycewang-stanford
RepositoryAwesome-Journal-Skills
Skill nameccs-topic-selection
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 Topic Selection 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-topic-selection
  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-topic-selection .claude/skills/ccs-topic-selection
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ccs Topic Selection 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 Topic Selection 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 Topic Selection 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 Topic Selection

Use this before writing. ACM CCS is the SIGSAC flagship: it rewards work with a concrete attacker, a defensible threat model, and evidence that survives an adversarial program committee. Decide venue by community and contribution type, never by prestige ranking.

Fit test

  • Prefer CCS when the contribution is a broad computer-security result — a new attack class, a defense with measured cost, an applied-cryptography protocol, a systems or web-security mechanism, or a measurement study — aimed at the cross-area SIGSAC community.
  • Route to IEEE S&P (Oakland) when the work suits that PC's taste for foundational or systematization framing and the November cycle fits your calendar better.
  • Route to USENIX Security when the contribution is artifact-heavy systems security whose evidence lives in a runnable tool and open benchmark.
  • Route to NDSS when the core is network- and distributed-system security (protocols, DNS, routing, malware infrastructure).
  • Route to PETS/PoPETs when privacy is the primary lens rather than one property among many.
  • Route to CRYPTO/EUROCRYPT when the contribution is cryptographic theory whose proof, not its deployment, is the result.

Fit signal table

Signal in the projectCCS reading
New attack with a clearly bounded adversary and demonstrated impactCore fit — the house genre
Defense evaluated against adaptive attacks with deployment costCore fit
Applied crypto protocol with implementation and measured overheadCore fit
Internet-scale or ecosystem measurement with validated samplingCore fit
Pure cryptographic hardness proof, no systemCRYPTO/EUROCRYPT or a theory venue
Privacy-first metrics with no other security propertyPETS/PoPETs

Vignette: where a side-channel result goes

A project extracts keys from a deployed TLS library via a microarchitectural side channel, with a proof-of-concept exploit and a constant-time patch. CCS reading: strong fit — a concrete attacker, measured leakage, and a defense with overhead numbers is exactly the CCS arc. Strip the exploit and keep only an abstract leakage bound, and it drifts toward a crypto theory venue; expand the network-measurement of vulnerable hosts into the whole story, and NDSS becomes plausible; foreground only the privacy harm to users, and PETS fits better.

Sharpening moves before committing

  • Name the attacker: capabilities, knowledge, position, and what success means. If you cannot write the threat model in three sentences, the contribution is not yet CCS-shaped.
  • Decide the contribution type — attack, defense, protocol, measurement, tool, or study — because reviewers grade each against a different evidence bar.
  • Confirm the result fits the 12-page ACM sigconf body; CCS bodies are dense, and a paper needing 30 pages of proofs may belong at a journal or a theory venue.
  • Scope drifts across cycles; scan the current CFP topic list and recent proceedings before final routing.

Output format

text
[Fit] strong CCS / possible CCS / better elsewhere
[Best venue] CCS / IEEE S&P / USENIX Security / NDSS / PETS / crypto venue / other
[Contribution type] attack / defense / protocol / measurement / tool / study
[Threat model in one line] <adversary capability and goal>
[Top rejection risk] <threat-model / novelty / evidence / ethics / scope>
[Next action] <sharpen threat model, add evidence, reframe, or switch venue>

Frequently asked questions

What does the Ccs Topic Selection AI skill do?

Use when deciding whether a security project fits ACM CCS versus IEEE S&P, USENIX Security, NDSS, PETS, or a crypto/theory venue, identifying the security contribution type, and sharpening the threat model and attacker capability before writing begins.

Why use Ccs Topic Selection on TypingMind?

Because you install it once and use it with any model. Ccs Topic Selection 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 Topic Selection 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-topic-selection. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ccs Topic Selection?

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 Topic Selection?

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

Is the Ccs Topic Selection 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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