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github/awesome-copilot/skills/gdpr-compliant/SKILL.md

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

Source repository stars
37,126
Declared platforms
0
Static risk flags
0
Last source update
2026-07-28
Source checked
2026-07-28

Decision brief

What it does—and where it fits

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

Best for

    Not for

    • 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.

    Compatibility matrix

    Platform support, with evidence labels

    PlatformStatusEvidenceWhat to check
    CodexNot declaredNo explicit evidencePortability before use
    Claude CodeNot declaredNo explicit evidencePortability before use
    CursorNot declaredNo explicit evidencePortability before use
    Gemini CLINot declaredNo explicit evidencePortability before use
    Open the compatibility checker

    Installation

    Inspect first. Install second.

    The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.

    Source-detected install commandSource
    npx skills add https://github.com/github/awesome-copilot --skill "skills/gdpr-compliant"
    Safe inspection promptEditorial

    Inspect the Agent Skill "gdpr-compliant" from https://github.com/github/awesome-copilot/blob/9933dcad5be5caeb288cebcd370eeeb2fc2f1685/skills/gdpr-compliant/SKILL.md at commit 9933dcad5be5caeb288cebcd370eeeb2fc2f1685. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.

    Workflow

    What the source asks the agent to do

    1. 01

      15. PR Review Checklist

      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.

      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.
    2. 02

      1. Core GDPR Principles (Article 5)

      Review the “1. Core GDPR Principles (Article 5)” section in the pinned source before continuing.

      Review and apply the “1. Core GDPR Principles (Article 5)” source section.
    3. 03

      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…

      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).
    4. 04

      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…

      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.
    5. 05

      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.

      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.Share personal data collected for service delivery with advertising networks without explicit consent.

    Permission review

    Static risk signals and limitations

    No configured static risk pattern was detected

    This is not proof of safety. Runtime behavior, indirect dependencies, and hidden external systems are outside the static scan.

    Evidence record

    Why each signal appears

    EvidenceSourceComputedTestedEditorial
    SignalValueEvidence typeMeaning
    Quality score77/100ComputedDocumentation, specificity, maintenance, and trust rules
    Repository stars37,126SourceRepository attention, not individual Skill quality
    Compatibility0 platformsSourceDeclared in the catalog source record
    Usage guideautomated source guideEditorialGenerated or reviewed according to the visible evidence level

    Pinned source

    Provenance and original SKILL.md

    Repository
    github/awesome-copilot
    Skill path
    skills/gdpr-compliant/SKILL.md
    Commit
    9933dcad5be5caeb288cebcd370eeeb2fc2f1685
    License
    MIT
    Collected
    2026-07-28
    Default branch
    main
    View the original SKILL.md

    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 [email protected] to [email protected]".
    • 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 [email protected] 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.

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