Token导航 LogoToken导航

Agent Skill · 开发

dpia-sentinel-oliver-schmidt-prietz(新闻部哨兵奥利弗·施密特·普里茨)

dpia-sentinel-oliver-schmidt-prietz 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

dpia-sentinel-oliver-schmidt-prietz 是开发类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证信息还不明确,第三方扫描已通过,按安装量在开发类的 21,746 个 Skill 里排第 13,397。

总安装 917周安装 39GitHub Stars 302下载 321核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:dpia-sentinel-oliver-schmidt-prietz(新闻部哨兵奥利弗·施密特·普里茨)
来源仓库:https://github.com/lawvable/awesome-legal-skills
仓库路径:skills/dpia-sentinel-oliver-schmidt-prietz
安装命令:
npx skills add https://github.com/lawvable/awesome-legal-skills --skill dpia-sentinel-oliver-schmidt-prietz
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/lawvable/awesome-legal-skills --skill dpia-sentinel-oliver-schmidt-prietz

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

dpia-sentinel 提供 GDPR 第 35 条数据保护影响评估(DPIA)的结构化实施指南。适用于处理高风险个人数据的企业法务与技术团队,降低合规处罚风险。依据 EDPB 指南与国家监管机构要求,输出风险评估表与缓解措施建议。不可替代专业法律意见,重大决策须联合 DPO 与外部律师共同确认。

适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/dpia-sentinel-oliver-schmidt-prietz

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/lawvable/awesome-legal-skills --skill dpia-sen…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

dpia-sentinel-oliver-schmidt-prietz 怎么安装?

在终端执行 npx skills add https://github.com/lawvable/awesome-legal-skills --skill dpia-sentinel-oliv…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

dpia-sentinel-oliver-schmidt-prietz 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

dpia-sentinel-oliver-schmidt-prietz 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

dpia-sentinel-oliver-schmidt-prietz 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

dpia-sentinel-oliver-schmidt-prietz 用的人多吗?

公开数据是累计安装 917 次,近一周安装 39 次,下载 321 次,GitHub 302 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

dpia-sentinel-oliver-schmidt-prietz 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理开发类任务、又不想每次都手写提示词的人。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
dpia-sentinel-oliver-schmidt-prietz

新闻部哨兵奥利弗·施密特·普里茨 · 本页

917302只读未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

DPIA Sentinel

Disclaimer (show at session start)

Important: This provides structured GDPR Article 35 guidance based on EDPB Guidelines and national SA requirements. It is not legal advice. Involve your DPO (Art. 35(2)) and qualified counsel for final decisions.

Routing

Determine what the user needs and load references accordingly:

User NeedLoad These ReferencesAction
"Do I need a DPIA?" / threshold questionreferences/edpb-criteria.md + relevant jurisdiction file(s)Run threshold assessment
Full DPIAedpb-criteria.md + jurisdiction(s) + references/risk-catalog.md + scoring.mdWalk through assessment phases
Document generation (.docx)references/templates.md + docx generation skill (/mnt/skills/public/docx/SKILL.md in Claude.ai Projects, or docx-processing-anthropic skill in Claude Code; if unavailable, generate well-formatted Markdown as fallback)Generate Word document
Specific legal questionLoad relevant reference onlyAnswer directly

Jurisdiction selection: Ask two questions: (1) Where is the controller's main establishment? (2) Where are the data subjects located? Load all jurisdiction files that are relevant — this may be multiple files for multi-jurisdictional processing. See references/edpb-criteria.md → "Multi-Jurisdictional DPIA Analysis" for the full decision framework.

Available jurisdiction files:

  • references/jurisdictions/de-dsk.md — Germany
  • references/jurisdictions/fr-cnil.md — France
  • references/jurisdictions/ie-dpc.md — Ireland
  • references/jurisdictions/be-apd.md — Belgium
  • references/jurisdictions/nl-ap.md — Netherlands
  • references/jurisdictions/it-garante.md — Italy
  • references/jurisdictions/pl-uodo.md — Poland
  • references/jurisdictions/whitelists.md — France, Czech Republic, Spain, Austria

For jurisdictions not covered by a dedicated file, rely on the EDPB nine-criteria analysis in references/edpb-criteria.md and note that the user should check their national SA's Art. 35(4) list directly.

Assessment Flow

Threshold → Description → Necessity/Proportionality → Risks → Mitigations → Residual Risk → Art. 36 Check → Documentation

This is the logical sequence, not a rigid script. Adapt to the user: if they provide rich context upfront, skip intake questions. If they're experienced, move faster. If they're new to DPIAs, explain more.

The assessment is iterative: if mitigations in later stages change the processing design, revisit earlier analysis and flag this to the user.

Legal Precision Points

These are areas where Claude's training knowledge may be imprecise. Always apply these rules:

  1. Art. 35(3) triggers are absolute. If any of the three mandatory cases apply (systematic extensive automated evaluation with legal/significant effect; large-scale special category/criminal data; systematic monitoring of publicly accessible areas on large scale), a DPIA is required — no balancing, no judgment call.
  2. The two-criteria rule is a presumption, not a mandate. Meeting 2+ of the 9 EDPB criteria creates a strong presumption a DPIA is needed. But a DPIA *may* be needed with only 1 criterion, and *may* be justified as unnecessary with 2 — if thoroughly documented. See WP 248 rev.01, p. 11.
  3. Art. 9 is cumulative with Art. 6. Special category data always needs BOTH a legal basis under Art. 6 AND an exception under Art. 9(2). These are separate legal hurdles.
  4. "Large scale" has no fixed number. The EDPB uses four factors: number of subjects, data volume, duration, geographic extent. An individual doctor is not large scale; a regional hospital is. Never cite a specific numerical threshold.
  5. National blacklists are additive, not exhaustive. Processing not on a blacklist may still require a DPIA. A blacklist entry in the relevant jurisdiction overrides whitelist exemptions from other jurisdictions.
  6. Multi-jurisdictional processing requires checking ALL relevant blacklists. Art. 35(4) lists are territorial — the DPIA obligation is triggered if the processing matches a blacklist in ANY jurisdiction where the controller is established OR where data subjects are located. The one-stop-shop mechanism (Art. 56) governs enforcement jurisdiction, but it does NOT limit which Art. 35(4) lists apply to the DPIA obligation itself. A single DPIA can address multiple jurisdictions, but the threshold analysis must run against each applicable national list. See references/edpb-criteria.md → "Multi-Jurisdictional DPIA Analysis" for details.
  7. DPIA must happen before processing begins (Art. 35(1)). It is a pre-processing obligation, not a retroactive compliance exercise. If processing has already started, the DPIA should still be done but note this as a compliance gap.
  8. AI requires dual-phase analysis (EDPB Opinion 28/2024). Training and deployment are separate processing activities with distinct risk profiles. A deployer cannot simply rely on the model provider's DPIA.
  9. Art. 36 prior consultation is sequential to the DPIA, not part of it. The DPIA identifies residual risk; if that risk remains high after all feasible mitigations, Art. 36 requires consulting the SA before processing begins. The SA has 8 weeks (extendable by 6).
  10. Pseudonymization as risk reducer (EDPB Guidelines 01/2025 on Pseudonymisation, adopted 17 January 2025): Effective pseudonymization with technically separated additional information can meaningfully reduce likelihood scores in risk assessment. But it must be genuine — if re-identification is trivial, it doesn't reduce risk.
  11. Risk assessment is from the data subject's perspective. A DPIA assesses risks to rights and freedoms of natural persons (Recital 75), not corporate/business risks. Identity theft risk to the individual, not reputational risk to the company.
  12. AI Act FRIA is distinct from DPIA. For high-risk AI systems under the AI Act, a Fundamental Rights Impact Assessment (FRIA) may also be required. DPIA (data protection risks) and FRIA (broader fundamental rights) are complementary — one does not replace the other.

Output Formats

Threshold result: Present a clear verdict (DPIA Required / Recommended / Not Required) with the reasoning showing Art. 35(3) check, criteria analysis, and national list check.

Risk register: Table with Risk ID, Description, Rights Category, Likelihood (1-5), Severity (1-5), Score, Level. Use the scoring methodology in references/scoring.md.

Residual risk overview: Summary showing total risks by level before and after mitigation, plus overall position (Acceptable / Acceptable with Conditions / Art. 36 Consultation Required).

Documents: Generate.docx files following references/templates.md. Always read the docx skill first.

平台分布

Codex

36.49%
按下载量换算117

Claude

32.45%
按下载量换算104

Cursor

19.48%
按下载量换算63

Gemini CLI

8.83%
按下载量换算28

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

继续浏览同类 Skills