/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
$ npx -y skills add brycewang-stanford/Awesome-Journal-Skills --skill ccs-reproducibility --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/ccs-reproducibility
Context preview
The summary Claude sees to decide when to auto-load this skill.
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
SKILL.md
ccs-reproducibility.SKILL.mdname: ccs-reproducibility
description: 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.
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 type | What full sharing looks like | Honest fallback when sharing is blocked | |---|---|---| | Exploit against deployed software | Runnable PoC plus target build | Redacted PoC, disclosed-and-patched note, synthetic target | | Defense with overhead numbers | Instrumented build and benchmark scripts | Binaries plus measurement scripts if source is proprietary | | Internet-scale measurement | Dataset plus collection tooling | Aggregated data with subject-privacy justification for the rest | | Cryptographic protocol | Reference implementation and test vectors | Spec 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
[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>
Read more
name: ccs-reproducibility description: 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.
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 type | What full sharing looks like | Honest fallback when sharing is blocked | |---|---|---| | Exploit against deployed software | Runnable PoC plus target build | Redacted PoC, disclosed-and-patched note, synthetic target | | Defense with overhead numbers | Instrumented build and benchmark scripts | Binaries plus measurement scripts if source is proprietary | | Internet-scale measurement | Dataset plus collection tooling | Aggregated data with subject-privacy justification for the rest | | Cryptographic protocol | Reference implementation and test vectors | Spec 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
[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>
Stanford REAP × CoPaper.AI · 由斯坦福实证方法论团队精选与维护 访问 copaper.ai 微信:CoPaper.AI 按 11 个主流学科板块覆盖 经管与商科 社会科学 人文学科 数学与物理科学 生命科学 医学与健康 工程与技术 计算机科学与 AI 体育科学 点击任一学科名可跳转到对应说明;每类下的代表子领域在正文总览中完整列出。下方封面墙按 venue 导航,完整分类见覆盖一览。 🧭 布局指南 · 📚 Skill Pack 一览 · ⚡ 如何使用 · 🧪 自动实证
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