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Ccs Reproducibility

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brycewang-stanford
ccs-reproducibility

Use when strengthening ACM CCS reproducibility evidence, including the artifact-availability posture, threat-model-to-evidence mapping, attack reproduction steps, defense overhead measurement, measurement-dataset provenance, environment and version pinning, and honest justification when artifacts cannot be shared.

Overview

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

Use it in TypingMind

Enable Ccs Reproducibility 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 Reproducibility 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 Reproducibility 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 Reproducibility

Use this before submission and again before the artifact-evaluation deadline. Reopen the current CFP and call for artifacts to confirm what availability statement and packaging CCS expects this cycle.

Evidence map

  • Map each security claim — every attack, defense guarantee, and measurement result — to a verifiable location: a script, a config, a dataset, a proof, or a logged run.
  • For attacks, record the target's exact version and configuration, the attacker's resource budget, and the sequence of steps that reproduce the exploit.
  • For defenses, record the workload, the overhead-measurement method, the hardware, and the adaptive attacker used, so the cost-versus-security tradeoff can be rechecked.
  • For measurements, document the vantage point, the collection window, the sampling frame, known blind spots, and the ground-truth validation.
  • When artifacts cannot be shared — licensing, responsible disclosure, subject safety, or premature-release risk — say so explicitly and offer partial, synthetic, or redacted artifacts that still let a reader assess the methodology.

Availability-posture table

Claim typeWhat full sharing looks likeHonest fallback when sharing is blocked
Exploit against deployed softwareRunnable PoC plus target buildRedacted PoC, disclosed-and-patched note, synthetic target
Defense with overhead numbersInstrumented build and benchmark scriptsBinaries plus measurement scripts if source is proprietary
Internet-scale measurementDataset plus collection toolingAggregated data with subject-privacy justification for the rest
Cryptographic protocolReference implementation and test vectorsSpec plus test vectors if the implementation is embargoed

Claiming an artifact is unavailable without a reason CCS accepts (a license, a disclosure embargo, subject safety) reads as evasion; state the specific reason and offer the closest shareable substitute.

Vignette: a measurement paper on vulnerable hosts

Consider a study scanning the Internet for a misconfiguration. Its reproducibility spine: the scan methodology and rate-limiting, the classification rule for "vulnerable," the ground-truth sample validated by hand, the ethics of scanning and notification, and an aggregated dataset that preserves the finding without exposing individual vulnerable hosts to opportunistic attackers.

Degrees of reproducibility

  • Turnkey: one command reproduces the attack or the overhead measurement from pinned configs.
  • Scripted: scripts exist but need documented manual steps or gated data access.
  • Descriptive: prose detailed enough that a competent security researcher could rebuild it.

For CCS, aim turnkey for anything you submit to artifact evaluation, and state the achieved level honestly rather than overpromising a one-command reproduction that fails on a clean host.

Output format

text
[Claim inventory] <claim -> evidence location>
[Availability posture] full / partial / justified-withheld
[Reproducibility gaps] <versions / configs / budgets / provenance / ethics>
[Paper fixes] <must appear in main PDF or appendix>
[Artifact fixes] <packaging additions before the AE deadline>

Frequently asked questions

What does the Ccs Reproducibility AI skill do?

Use when strengthening ACM CCS reproducibility evidence, including the artifact-availability posture, threat-model-to-evidence mapping, attack reproduction steps, defense overhead measurement, measurement-dataset provenance, environment and version pinning, and honest justification when artifacts cannot be shared.

Why use Ccs Reproducibility on TypingMind?

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

Which AI models can use Ccs Reproducibility?

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 Reproducibility?

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

Is the Ccs Reproducibility 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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