Token导航 LogoToken导航

Agent Skill · 研究检索

gdpr-compliant(gdpr compliant 搜索)

gdpr-compliant 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。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。

gdpr-compliant 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为接触敏感数据;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,674 个 Skill 里排第 6,622。

总安装 21,521周安装 915GitHub Stars 31,707下载 7,540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:gdpr-compliant(gdpr compliant 搜索)
来源仓库:https://github.com/github/awesome-copilot
仓库路径:skills/gdpr-compliant
安装命令:
npx skills add https://github.com/github/awesome-copilot --skill gdpr-compliant
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/github/awesome-copilot --skill gdpr-compliant

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

用于查找和验证 GDPR 合规性相关信息,支持政策查询和案例参考。适合在多种宿主环境中快速定位合规性资源。通过 GitHub 仓库安装,需确认权限范围和操作边界后再使用。建议结合原始 README 核验具体用法,避免触发不必要的联网或文件读写。使用前请检查维护状态,确保技能与当前宿主环境兼容。

来源仓库内路径:skills/gdpr-compliant

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/github/awesome-copilot --skill gdpr-compliant;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

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

gdpr-compliant 怎么安装?

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

gdpr-compliant 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。

gdpr-compliant 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

gdpr-compliant 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。

gdpr-compliant 用的人多吗?

公开数据是累计安装 21,521 次,近一周安装 915 次,下载 7,540 次,GitHub 31,707 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。

gdpr-compliant 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。
  • 来源仓库有 31,707 星,维护活跃度相对可查。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 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。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 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。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
gdpr-compliant

gdpr compliant 搜索 · 本页

21,52131,707接触敏感数据未明确通过

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

GDPR Engineering Skill

Actionable GDPR reference for engineers, architects, DevOps, and tech leads. Inspired by CNIL developer guidance and GDPR Articles 5, 25, 32, 33, 35.

Golden Rule: Collect less. Store less. Expose less. Retain less.

For deep dives, read the reference files in references/:

  • references/data-rights.md — user rights endpoints, DSR workflow, RoPA
  • references/security.md — encryption, hashing, secrets, anonymization
  • references/operations.md — cloud, CI/CD, incident response, architecture patterns

1. Core GDPR Principles (Article 5)

PrincipleEngineering obligation
Lawfulness, fairness, transparencyDocument legal basis for every processing activity in the RoPA
Purpose limitationData collected for purpose A MUST NOT be reused for purpose B without a new legal basis
Data minimizationCollect only fields with a documented business need today
AccuracyProvide update endpoints; propagate corrections to downstream stores
Storage limitationDefine TTL at schema design time — never after
Integrity & confidentialityEncrypt at rest and in transit; restrict and audit access
AccountabilityMaintain evidence of compliance; RoPA ready for DPA inspection at any time

2. Privacy by Design & by Default

MUST

  • Add CreatedAt, RetentionExpiresAt to every table holding personal data at creation time.
  • Default all optional data collection to off. Users opt in; they never opt out of a default-on setting.
  • Conduct a DPIA before building high-risk processing (biometrics, health data, large-scale profiling, systematic monitoring).
  • Update the RoPA with every new feature that introduces a processing activity.
  • Sign a DPA with every sub-processor before data flows to them.

MUST NOT

  • Ship a new data collection feature without a documented legal basis.
  • Enable analytics, tracking, or telemetry by default without explicit consent.
  • Store personal data in a system not listed in the RoPA.

3. Data Minimization

MUST

  • Map every DTO/model field to a concrete business need. Remove undocumented fields.
  • Use separate DTOs for create, read, and update — never reuse the same object.
  • Return only what the caller is authorized to see — use response projections.
  • Mask sensitive values at the edge: return ****1234 for card numbers, never the full value.
  • Exclude sensitive fields (DOB, national ID, health) from default list/search projections.

MUST NOT

  • Log full request/response bodies if they may contain personal data.
  • Include personal data in URL path segments or query parameters (CDN logs, browser history).
  • Collect dateOfBirth, national ID, or health data without an explicit legal basis.

4. Purpose Limitation

MUST

  • Document the purpose of every processing activity in code comments and in the RoPA.
  • Obtain a new legal basis or perform a compatibility analysis before reusing data for a secondary purpose.

MUST NOT

  • Share personal data collected for service delivery with advertising networks without explicit consent.
  • Use support ticket content to train ML models without a separate legal basis and user notice.

5. Storage Limitation & Retention

MUST

  • Every table holding personal data MUST have a defined retention period.
  • Enforce retention automatically via a scheduled job (Hangfire, cron) — never a manual process.
  • Anonymize or delete data when retention expires — never leave expired data silently in production.

Recommended defaults

Data typeMax retention
Auth / audit logs12–24 months
Session / refresh tokens30–90 days
Email / notification logs6 months
Inactive user accounts12 months after last login → notify → delete
Payment recordsAs required by tax law (7–10 years), minimized
Analytics events13 months

SHOULD

  • Add RetentionExpiresAt column — compute at insert time.
  • Use soft-delete (DeletedAt) with a scheduled hard-delete after the erasure request window (30 days).

MUST NOT

  • Retain personal data indefinitely "in case it becomes useful later."

6. API Design Rules

MUST

  • MUST NOT include personal data in URL paths or query parameters.

- GET /users/{userId}

  • Authenticate all endpoints that return or accept personal data.
  • Extract the acting user's identity from the JWT — never from the request body.
  • Validate ownership on every resource: if (resource.OwnerId!= currentUserId) return 403.
  • Use UUIDs or opaque identifiers — never sequential integers as public resource IDs.

SHOULD

  • Rate-limit sensitive endpoints (login, data export, password reset).
  • Set Referrer-Policy: no-referrer and an explicit CORS allowlist.

MUST NOT

  • Return stack traces, internal paths, or database errors in API responses.
  • Use Access-Control-Allow-Origin: * on authenticated APIs.

7. Logging Rules

MUST

  • Anonymize IPs in application logs — mask last octet (IPv4) or last 80 bits (IPv6).

- 192.168.1.xxx

  • MUST NOT log: passwords, tokens, session IDs, credentials, card numbers, national IDs, health data.
  • MUST NOT log full request/response bodies where PII may be present.
  • Enforce log retention — purge automatically after the defined period.

SHOULD

  • Log events not data: "User {UserId} updated email" not "Email changed from a@b.com to c@d.com".
  • Use structured logging (JSON) with userId as an internal identifier, not the email address.
  • Separate audit logs (sensitive access, admin actions) from application logs — different retention and ACLs.

8. Error Handling

MUST

  • Return generic error messages — never expose stack traces, internal paths, or DB errors.

- "Column 'email' violates unique constraint on table 'users'" - "A user with this email address already exists."

  • Use Problem Details (RFC 7807) for all error responses.
  • Log the full error server-side with a correlation ID; return only the correlation ID to the client.

MUST NOT

  • Include file paths, class names, or line numbers in error responses.
  • Include personal data in error messages (e.g., "User john@example.com not found").

9. Encryption (summary — see references/security.md for full detail)

ScopeMinimum standard
Standard personal dataAES-256 disk/volume encryption
Sensitive data (health, financial, biometric)AES-256 column-level + envelope encryption via KMS
In transitTLS 1.2+ (prefer 1.3); HSTS enforced
KeysHSM-backed KMS; rotate DEKs annually

MUST NOT allow TLS 1.0/1.1, null cipher suites, or hardcoded encryption keys.


10. Password Hashing

MUST

  • Use Argon2id (recommended) or bcrypt (cost ≥ 12). Never MD5, SHA-1, or SHA-256.
  • Use a unique salt per password. Store only the hash.

MUST NOT

  • Log passwords in any form. Transmit passwords in URLs. Store reset tokens in plaintext.

11. Secrets Management

MUST

  • Store all secrets in a KMS: Azure Key Vault, AWS Secrets Manager, GCP Secret Manager, or HashiCorp Vault.
  • Use pre-commit hooks (gitleaks, detect-secrets) to prevent secret commits.
  • Rotate secrets on developer offboarding, annual schedule, or suspected compromise.

.gitignore MUST include: .env, .env.*, *.pem, *.key, *.pfx, *.p12, secrets/

MUST NOT

  • Commit secrets to source code. Store secrets as plain-text environment variable defaults.

12. Anonymization & Pseudonymization (summary — see references/security.md)

  • Anonymization = irreversible → falls outside GDPR scope. Use for retained records after erasure.
  • Pseudonymization = reversible with a key → still personal data, reduced risk.
  • When erasing a user, anonymize records that must be retained (financial, audit) rather than deleting them.
  • Store the pseudonymization key in the KMS — never in the same database as the pseudonymized data.

MUST NOT call data "anonymized" if re-identification is possible through linkage attacks.


13. Testing with Fake Data

MUST

  • MUST NOT use production personal data in dev, staging, or CI environments.
  • MUST NOT restore production DB backups to non-production without scrubbing PII first.
  • Use synthetic data generators: Bogus (.NET), Faker (JS/Python/Ruby).
  • Use @example.com for all test email addresses.

14. Anti-Patterns

Anti-patternCorrect approach
PII in URLsOpaque UUIDs as public identifiers
Logging full request bodiesLog structured event metadata only
"Keep forever" schemaTTL defined at design time
Production data in dev/testSynthetic data + scrubbing pipeline
Shared credentials across teamsIndividual accounts + RBAC
Hardcoded secretsKMS + secret manager
Access-Control-Allow-Origin: * on auth APIsExplicit CORS allowlist
Storing consent with profile dataDedicated consent store
PII in GET query paramsPOST body or authenticated session
Sequential integer IDs in public URLsUUIDs
"Anonymized" data with quasi-identifiersApply k-anonymity, test linkage resistance
Mixing backup regions outside EEAExplicit region lockdown on backup jobs

15. PR Review Checklist

Data model

  • Every new PII column has a documented purpose and retention period.
  • Sensitive fields (health, financial, national ID) use column-level encryption.
  • No sequential integer PKs as public-facing identifiers.

API

  • No PII in URL paths or query parameters.
  • All endpoints returning personal data are authenticated.
  • Ownership checks present — user cannot access another user's resource.
  • Rate limiting applied to sensitive endpoints.

Logging

  • No passwords, tokens, or credentials logged.
  • IPs anonymized (last octet masked).
  • No full request/response bodies logged where PII may be present.

Infrastructure

  • No public storage buckets or public-IP databases.
  • New cloud resources tagged with DataClassification.
  • Encryption at rest enabled for new storage resources.
  • New geographic regions for data storage are EEA-compliant or covered by SCCs.

Secrets & CI/CD

  • No secrets in source code or committed config files.
  • New secrets added to KMS and secrets inventory document.
  • CI/CD secrets masked in pipeline logs.

Retention & erasure

  • Retention enforcement job or policy covers new data store or field.
  • Erasure pipeline updated to cover new data store.

User rights & governance

  • Data export endpoint includes any new personal data field.
  • RoPA updated if a new processing activity is introduced.
  • New sub-processors have a signed DPA and a RoPA entry.
  • DPIA triggered if the change involves high-risk processing.

Golden Rule: Collect less. Store less. Expose less. Retain less. Every byte of personal data you do not collect is a byte you cannot lose, cannot breach, and cannot be held liable for.

*Inspired by CNIL developer GDPR guidance, GDPR Articles 5, 25, 32, 33, 35, ENISA, OWASP, and NIST engineering best practices.*

平台分布

Codex

33.55%
按下载量换算2,530

Claude

31.68%
按下载量换算2,389

Cursor

16.63%
按下载量换算1,254

Gemini CLI

8.87%
按下载量换算669

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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