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Ccs Artifact Evaluation

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brycewang-stanford
ccs-artifact-evaluation

Use when packaging ACM CCS artifacts for the artifact-evaluation committee and the ACM badges — Artifacts Available, Artifacts Evaluated Functional, Artifacts Evaluated Reusable, and Results Reproduced — covering what security evaluators inspect, how to make attacks and defenses turnkey, and how to justify withheld artifacts.

Overview

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

Use it in TypingMind

Enable Ccs Artifact Evaluation 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 Artifact Evaluation 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 Artifact Evaluation 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 Artifact Evaluation

Use this for artifact packaging around CCS. CCS runs an optional artifact-evaluation process after acceptance; passing artifacts earn ACM badges that appear on the paper's first page. Reopen the current call for artifacts for the exact badge set, submission form, and deadlines.

The ACM badge ladder

BadgeWhat it certifiesWhat the committee does
Artifacts AvailableThe artifact is publicly archived with a stable identifierConfirms the artifact is retrievable and permanent
Artifacts Evaluated - FunctionalThe artifact runs and does what the paper saysExecutes it against the documented steps
Artifacts Evaluated - ReusableIt exceeds functional quality and others can reuse itJudges structure, documentation, and reusability
Results ReproducedThe main paper results are independently reproducedReruns experiments and checks they support the claims

Available is about archival permanence; Functional and Reusable are about quality; Results Reproduced is the highest bar and requires the committee to regenerate your headline numbers.

Artifact plan

  • Decide which claims the artifact must support: the exploit, the defense overhead, the measurement pipeline, or the protocol implementation.
  • Make the security claim turnkey: one documented command per headline result, with expected output, runtime, and the hardware assumed.
  • Anonymize nothing at this stage — artifact evaluation is post-acceptance and single-blind or open — but do pin versions, dependencies, and a container so it runs on a clean machine.
  • For dangerous artifacts (working exploits, malware, live attack tooling), gate access, document safe-handling, and follow the responsible-disclosure posture from the paper.
  • Justify anything withheld: licensing, disclosure embargo, subject safety, or premature-release risk, and offer partial, synthetic, or redacted substitutes that still let evaluators check the method.

What security evaluators open first

Claim typeFirst artifact inspectedCommon failure caught
Exploit against real softwareThe PoC and its target build instructionsTarget version unspecified; PoC fails to build
Defense with overhead numbersThe benchmark runner and instrumented buildOverhead cannot be reproduced; workload undocumented
Measurement studyThe collection and classification scriptsClassification rule differs from the paper's prose
Cryptographic implementationTest vectors and the reference buildNo test vectors; outputs cannot be checked

Worked vignette: packaging a fuzzing-tool artifact

A hypothetical accepted paper presents a new fuzzer that found N bugs. For Reusable and Results Reproduced: ship a container pinning the fuzzer, the target corpus, and the seed set; document one command to reproduce a representative crash and one to rerun a bounded campaign; provide the bug list with disclosure status; and note which crashes are embargoed pending vendor patches, offering redacted reproducers for those.

Output format

text
[Target badges] available / functional / reusable / results-reproduced
[Contents] <code / data / container / test vectors / logs>
[Turnkey level] one-command / scripted / descriptive / weak
[Safe-handling] <gating and disclosure posture for dangerous artifacts>
[Withheld-and-justified] <what and why, with substitute offered>
[Fixes before submission] <ordered list>

Frequently asked questions

What does the Ccs Artifact Evaluation AI skill do?

Use when packaging ACM CCS artifacts for the artifact-evaluation committee and the ACM badges — Artifacts Available, Artifacts Evaluated Functional, Artifacts Evaluated Reusable, and Results Reproduced — covering what security evaluators inspect, how to make attacks and defenses turnkey, and how to justify withheld artifacts.

Why use Ccs Artifact Evaluation on TypingMind?

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

Which AI models can use Ccs Artifact Evaluation?

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 Artifact Evaluation?

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

Is the Ccs Artifact Evaluation 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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