Official agent skill

Gdpr Compliant

by github in github/awesome-copilot

Apply GDPR-compliant engineering practices across your codebase.

OfficialMITAuto-check: notesLegal & Compliance

Install Gdpr Compliant

skills CLI
$ npx skills add github/awesome-copilot --skill gdpr-compliant -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install github/awesome-copilot gdpr-compliant --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/github/awesome-copilot.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/gdpr-compliant .claude/skills/gdpr-compliant && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
gdpr-compliant
GitHub stars
40k
Used in
1 other repo
Token cost
~3k tokens
SKILL.md length
1,477 words
Files
3 (incl. references)
Skills in repo
417
Repo updated
First seen
Licence
MIT

At a glance

Apply GDPR-compliant engineering practices across your codebase.

  • Works in 12 steps: Core GDPR Principles (Article 5) → Privacy by Design & by Default → Data Minimization → …
  • You are designing APIs
  • SKILL.md covers 1. Core GDPR Principles…, 2. Privacy by Design & by…, 3. Data Minimization and 4. Purpose Limitation, plus 11 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Gdpr Compliant is an agent skill from github/awesome-copilot, published by the product's own GitHub organization. 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…

Its SKILL.md is about 3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including reference files (for example `references/Security.md` and `references/data-rights.md`).

It sits in Legal & Compliance, covering Privacy and GDPR. The repository describes itself as: Community-contributed instructions, agents, skills, and configurations to help you make the most of GitHub Copilot. The licence is MIT.

When your agent uses it

  • You are designing APIs
  • Writing data models
  • Building authentication flows
  • Implementing logging

Example prompts

  • “is this GDPR-compliant?”
  • “/gdpr-compliant”

Workflow steps

12 steps, taken from the step headings in SKILL.md.

  1. Core GDPR Principles (Article 5)
  2. Privacy by Design & by Default
  3. Data Minimization
  4. Purpose Limitation
  5. Storage Limitation & Retention
  6. API Design Rules
  7. Logging Rules
  8. Error Handling
  9. Encryption (summary — see references/security.md for full detail)
  10. Password Hashing
  11. Secrets Management
  12. Anonymization & Pseudonymization (summary — see references/security.md)

What it can do on your machine

Read from SKILL.md and the folder at commit 727ff2e. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Gdpr Compliant loads about 3k tokens when it runs, and up to ~7.6k if it reads all its reference files. Until then it costs about 173 tokens; SKILL.md has 1,477 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~173
When it runs · the whole SKILL.md, loaded when a task matches
~3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~7.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NoteMentions a .env fileSKILL.md:187
    **`.gitignore` MUST include:** `.env`, `.env.*`, `*.pem`, `*.key`, `*.pfx`, `*.p12`, `secrets/`

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from github/awesome-copilot at commit 727ff2e, republished under its MIT licence (© github). 1,477 words, ~2,968 tokens.

Download SKILL.mdSave it as .claude/skills/gdpr-compliant/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
gdpr-compliant
description
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.

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.


Show full SKILL.md (585 more words)Show less

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.

© github, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (references) in skills/gdpr-compliant of github/awesome-copilot.

  • SKILL.md
  • references/Security.md
  • references/data-rights.md

Open the folder on GitHubat commit 727ff2e

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in github/awesome-copilot, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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Questions about Gdpr Compliant

What does Gdpr Compliant do?

Apply GDPR-compliant engineering practices across your codebase. Gdpr Compliant is an agent skill from github/awesome-copilot, published by the product's own GitHub organization. Apply GDPR-compliant engineering practices across your codebase.

When should I use Gdpr Compliant?

Gdpr Compliant fits situations like: you are designing APIs; writing data models; building authentication flows; implementing logging.

How do I install Gdpr Compliant in Claude Code?

Run `npx skills add github/awesome-copilot --skill gdpr-compliant -a claude-code`. Or copy the skill folder (skills/gdpr-compliant in github/awesome-copilot) into .claude/skills/gdpr-compliant in your project. Claude Code loads it when a task matches its description.

How do I install Gdpr Compliant in Codex?

Run `npx skills add github/awesome-copilot --skill gdpr-compliant -a codex`. Or copy the skill folder (skills/gdpr-compliant in github/awesome-copilot) into .agents/skills/gdpr-compliant in your project. Codex loads it when a task matches its description.

Can I use Gdpr Compliant in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add github/awesome-copilot --skill gdpr-compliant -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/gdpr-compliant, .gemini/skills/gdpr-compliant, .github/skills/gdpr-compliant and .opencode/skills/gdpr-compliant in your project.

What does Gdpr Compliant need to run?

SKILL.md names no scripts, command-line tools or credentials: Gdpr Compliant is instructions for the agent only.

Does Gdpr Compliant access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Gdpr Compliant safe to install?

Our automated static check of SKILL.md found notes only (mentions a .env file), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Gdpr Compliant use?

Gdpr Compliant is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Gdpr Compliant use?

About 3k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4.6k tokens, read only when the agent opens those files.

What are the alternatives to Gdpr Compliant?

Skills that share tags, products or a category with Gdpr Compliant: C15t (c15t/c15t, 1.9k stars), HIPAA Safe Harbor Coverage Audit (maziyarpanahi/openmed, 5.5k stars), Korean Privacy Terms (kimlawtech/korean-privacy-terms, 586 stars) and Gdpr Compliance (Sushegaad/Claude-Skills-Governance-Risk-and-Compliance, 939 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Gdpr Compliant?

github (a GitHub organization, an official publisher) maintains it in github/awesome-copilot, which has 39,748 GitHub stars. The repository holds 417 skills in this directory. The repository was last updated on October 7, 2026.

Source: github/awesome-copilot on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.