MCPInstall

gdpr-compliant

Apply GDPR-compliant engineering practices across your codebase. Use this skill whenever you are designing APIs, writing data models, building authentication flows, implementing logging, handling user data, writing retention/deletion jobs, designing cloud infrastructure, or reviewing pull requests for privacy compliance. Trigger this skill for any task involving personal data, user accounts, cookies, analytics, emails, audit logs, encryption, pseudonymization, anonymization, data exports, breach response, CI/CD pipelines that process real data, or any question framed as "is this GDPR-compliant?". Inspired by CNIL developer guidance and GDPR Articles 5, 25, 32, 33, 35.

Installs
1.7K
Last 24h
—
Trending rank
—
Security
Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/github/awesome-copilot --skill gdpr-compliant
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads gdpr-compliant automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

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.

FAQ

What does the gdpr-compliant skill do?

Apply GDPR-compliant engineering practices across your codebase. Use this skill whenever you are designing APIs, writing data models, building authentication flows, implementing logging, handling user data, writing retention/deletion jobs, designing cloud infrastructure, or reviewing pull requests for privacy compliance. Trigger this skill for any task involving personal data, user accounts, cookies, analytics, emails, audit logs, encryption, pseudonymization, anonymization, data exports, breach response, CI/CD pipelines that process real data, or any question framed as "is this GDPR-compliant?". Inspired by CNIL developer guidance and GDPR Articles 5, 25, 32, 33, 35.

How do I install gdpr-compliant?

Run npx skills add https://github.com/github/awesome-copilot --skill gdpr-compliant in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/gdpr-compliant/ (personal) or .claude/skills/gdpr-compliant/ (project).

Which AI agents can use gdpr-compliant?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is gdpr-compliant safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains gdpr-compliant?

It is published by github in github/awesome-copilot, and it is an official skill from the maker of the technology it covers. It has 1,662 recorded installs.

More from github/awesome-copilot

View all →

git-commit

github/awesome-copilot

Use this skill when the user wants to make a Git commit or split changes into more than one commit. Also use it when the user types "/commit" or asks for a conventional commit. The skill examines the diff, stages the related files, writes a Conventional Commits message, and runs git commit. Do not use it when the user wants only a commit message and no commit. Do not use it to push, open a pull request, rebase, or change the commit history.

OfficialCode Quality & Workflow47.4K installs

excalidraw-diagram-generator

github/awesome-copilot

Generate Excalidraw diagrams from natural language descriptions. Use when asked to "create a diagram", "make a flowchart", "visualize a process", "draw a system architecture", "create a mind map", or "generate an Excalidraw file". Supports flowcharts, relationship diagrams, mind maps, and system architecture diagrams. Outputs .excalidraw JSON files that can be opened directly in Excalidraw.

OfficialDocs & Writing31.9K installs

multi-stage-dockerfile

github/awesome-copilot

Create optimized multi-stage Dockerfiles for any language or framework

Official29.8K installs

documentation-writer

github/awesome-copilot

Diátaxis Documentation Expert. An expert technical writer specializing in creating high-quality software documentation, guided by the principles and structure of the Diátaxis technical documentation authoring framework.

OfficialDocs & Writing27.9K installs

prd

github/awesome-copilot

Generate high-quality Product Requirements Documents (PRDs) for software systems and AI-powered features. Includes executive summaries, user stories, technical specifications, and risk analysis.

Official24.1K installs

refactor

github/awesome-copilot

Surgical code refactoring to improve maintainability without changing behavior. Covers extracting functions, renaming variables, breaking down god functions, improving type safety, eliminating code smells, and applying design patterns. Less drastic than repo-rebuilder; use for gradual improvements.

OfficialCode Quality & Workflow22.4K installs

Popular devops & cloud skills

View category →

microsoft-foundry

microsoft/azure-skills

Build, deploy, evaluate, optimize, fine-tune, and manage Microsoft Foundry agents, models, and resources end to end. USE FOR: foundry, azd ai agent, azd provision/deploy, hosted agent scaffold/develop/run/deploy/troubleshoot, prompt agent create, managed harness agent create, GitHub Copilot harness agent, create agent, update agent, add tool to agent, invoke agent, agent insights, evaluate agent, batch eval, continuous eval, continuous monitoring, agent CI/CD, optimize prompt, improve prompt, optimize agent instructions, Agent Optimizer scaffold, dataset curation from traces, deploy model, model fine-tuning (SFT/DPO/RFT), Foundry project, RBAC, role assignment, permissions, quota, capacity, region, deployment failure, AI Services, create Foundry resource, knowledge index, customize deployment, onboard, availability, training-data, grader, distillation, large file upload. DO NOT USE FOR: Azure Functions, App Service, general Azure deploy (use azure-deploy), general Azure prep (use azure

OfficialDevOps & Cloud579.1K installs

azure-diagnostics

microsoft/azure-skills

Debug Azure production issues on Azure using AppLens, Azure Monitor, resource health, and safe triage. WHEN: debug production issues, troubleshoot app service, app service high CPU, app service deployment failure, troubleshoot container apps, container app 503 or target port mismatch, troubleshoot functions, function telemetry missing or stopped after a config/connection-string change, troubleshoot AKS, VM RDP, Linux SSH, VM black screen, can't connect to VM, reset VM password, NSG or firewall blocking, kubectl cannot connect, kube-system/CoreDNS failures, pod pending, crashloop, node not ready, upgrade failures, analyze logs, KQL, insights, image pull failures, cold start issues, health probe failures, resource health, root cause of errors, troubleshoot event hubs, troubleshoot service bus, messaging SDK error, AMQP connection failure, message lock lost, lock renewal during long-running processing, service bus dead letter.

OfficialDevOps & Cloud569.8K installs

azure-prepare

microsoft/azure-skills

Prepare azd-based Azure projects for deployment: generates azure.yaml, infrastructure (Bicep/Terraform), and Dockerfiles for the Azure Developer CLI (azd) workflow. USE ONLY when the user explicitly wants to use azd as the deployment tool, or the project already has an azure.yaml file. DO NOT USE FOR: non-azd deployments, Python App Service code-only deploys (use python-appservice-deploy), or cross-cloud migration (use azure-cloud-migrate). WHEN: prepare app for azd, create azure.yaml, set up azd infrastructure, modernize app for Azure with azd, deploy with azd, function app, timer trigger, service bus trigger, event-driven function, managed identity, generate Bicep, generate Terraform, create and deploy to Azure.

OfficialDevOps & Cloud569K installs

azure-ai

microsoft/azure-skills

Use for Azure AI: Search, Speech, OpenAI, Document Intelligence. Helps with search, vector/hybrid search, speech-to-text, text-to-speech, transcription, OCR. WHEN: AI Search, query search, vector search, hybrid search, semantic search, speech-to-text, text-to-speech, transcribe, OCR, convert text to speech.

OfficialDevOps & Cloud568.8K installs

azure-deploy

microsoft/azure-skills

Execute Azure deployments for ALREADY-PREPARED applications that have existing .azure/deployment-plan.md and infrastructure files. DO NOT use this skill when the user asks to CREATE a new application — use azure-prepare instead. This skill runs azd up, azd deploy, terraform apply, and az deployment commands with built-in error recovery. Requires .azure/deployment-plan.md from azure-prepare and validated status from azure-validate. WHEN: "run azd up", "run azd deploy", "execute deployment", "push to production", "push to cloud", "go live", "ship it", "bicep deploy", "terraform apply", "publish to Azure", "launch on Azure". DO NOT USE WHEN: "create and deploy", "build and deploy", "create a new app", "set up infrastructure", "create and deploy to Azure using Terraform" — use azure-prepare for these.

OfficialDevOps & Cloud568.6K installs

azure-validate

microsoft/azure-skills

Pre-deployment validation for Azure readiness. Run deep checks on configuration, infrastructure (Bicep or Terraform), RBAC role assignments, managed identity permissions, and prerequisites before deploying. WHEN: validate my app, check deployment readiness, run preflight checks, verify configuration, check if ready to deploy, validate azure.yaml, validate Bicep, test before deploying, troubleshoot deployment errors, validate Azure Functions, validate function app, validate serverless deployment, verify RBAC roles, check role assignments, review managed identity permissions, what-if analysis, validate Container Apps deployment.

OfficialDevOps & Cloud568K installs