Privacy by Design

Overview

This public intake copy packages

plugins/antigravity-awesome-skills-claude/skills/privacy-by-design

https://github.com/sickn33/antigravity-awesome-skills

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses

metadata.json

ORIGIN.md

Privacy by Design

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Legal Frameworks, User Rights (GDPR), Deep Dive: Why It Matters, Common Pitfalls, Third-Party Audit, Implementation Checklist.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• Use when building apps that collect personal data (names, emails, locations, preferences)
• Use when designing database schemas, APIs, or authentication flows
• Use when the user mentions forms, user accounts, analytics, or third-party integrations
• Use when deploying to production—verify privacy controls before launch
• Use when the request clearly matches the imported source intent: building apps that collect user data. Ensures privacy protections are built in from the start—data minimization, consent, encryption.
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.

Operating Table

metadata.json

ORIGIN.md

README.md

README.md

## Related Skills

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
2. Read the overview and provenance files before loading any copied upstream support files.
3. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
4. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
5. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
6. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
7. Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.

Imported Workflow Notes

Imported: Overview

Integrate privacy protections into software architecture from the beginning, not as an afterthought. This skill applies Privacy by Design principles (GDPR Article 25, Cavoukian's framework) when designing databases, APIs, and user flows. Protects real users' data and builds trust.

Imported: Legal Frameworks

GDPR (EU) — Primary reference. Article 25 mandates "data protection by design and by default." Applies to EU users and often adopted globally.

CCPA (California) — Right to know, delete, opt-out of sale. Similar principles: minimize, disclose, allow control.

LGPD (Brazil) — Aligned with GDPR. Purpose limitation, necessity, transparency. Applies to Brazil users.

Design for the strictest framework you target; it often satisfies others.

Examples

Example 1: Ask for the upstream workflow directly

Use @privacy-by-design to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @privacy-by-design against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @privacy-by-design for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @privacy-by-design using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Imported Usage Notes

Imported: Code Examples

JavaScript/Node — Minimal User Model

// BAD: Collecting everything "just in case"
const user = { email, name, phone, address, birthdate, ipAddress, userAgent, ... };

// GOOD: Minimal, documented purpose
const user = {
  email,        // purpose: authentication
  displayName,  // purpose: UI display
  createdAt,    // purpose: account age
};

JavaScript — Consent Before Tracking

// BAD: Track first, ask later
analytics.track(userId, event);

// GOOD: Check consent first
if (userConsent.analytics) {
  analytics.track(userId, event);
}

Python — Safe Logging

# BAD: Logging PII in plain text
logger.info(f"User {user.email} logged in from {request.remote_addr}")

# GOOD: Redact or hash identifiers
logger.info(f"User {hash_user_id(user.id)} logged in")
# Or: logger.info("User login", extra={"user_id_hash": hash_id(user.id)})

SQL — Schema with Purpose and Retention

-- GOOD: Document purpose and retention in schema
CREATE TABLE users (
  id UUID PRIMARY KEY,
  email VARCHAR(255) NOT NULL,  -- purpose: auth, retention: account lifetime
  display_name VARCHAR(100),   -- purpose: UI, retention: account lifetime
  created_at TIMESTAMPTZ,      -- purpose: audit, retention: 7 years
  last_login_at TIMESTAMPTZ    -- purpose: security, retention: 90 days
);

-- Add retention policy (PostgreSQL example)
-- Schedule job to anonymize/delete last_login_at after 90 days

API — Return Only Needed Fields

# BAD: Returning full user object
return jsonify(user)  # May include internal fields, hashed passwords

# GOOD: Explicit allowlist
return jsonify({
    "id": user.id,
    "email": user.email,
    "displayName": user.display_name,
})

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• ✅ Ask "do we need this?" for every new data field
• ✅ Design deletion and export flows from day one
• ✅ Use hashing or tokenization for sensitive identifiers when possible
• ✅ Document purpose and retention in schema or metadata
• ❌ Don't log passwords, tokens, or PII in plain text
• ❌ Don't share data with third parties without explicit consent
• ❌ Don't assume "we'll add privacy later"—it rarely happens

Imported Operating Notes

Imported: Core Principles

1. Data Minimization

Collect only what is strictly necessary. Every field needs a documented justification. Avoid "we might need it later."

2. Purpose Limitation

Store the purpose of each data point. Do not reuse data for purposes the user did not consent to.

3. Storage Limitation

Define retention periods. Implement automated deletion or anonymization when retention expires. Never keep data "forever" by default.

4. Privacy as Default

Opt-in for optional collection, not opt-out. Sensitive settings (analytics, marketing) off by default. No pre-checked consent boxes.

5. End-to-End Security

Encrypt at rest and in transit. Use RBAC. Log access to sensitive data for audit.

6. Transparency

Document what is collected and why. Clear privacy policies. Easy access and deletion for users.

Imported: Best Practices

• ✅ Ask "do we need this?" for every new data field
• ✅ Design deletion and export flows from day one
• ✅ Use hashing or tokenization for sensitive identifiers when possible
• ✅ Document purpose and retention in schema or metadata
• ❌ Don't log passwords, tokens, or PII in plain text
• ❌ Don't share data with third parties without explicit consent
• ❌ Don't assume "we'll add privacy later"—it rarely happens
• ❌ Don't expose stack traces or internal errors to clients

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in

plugins/antigravity-awesome-skills-claude/skills/privacy-by-design

metadata.json

ORIGIN.md

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated

SKILL.md

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @00-andruia-consultant-v2

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @10-andruia-skill-smith-v2

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @20-andruia-niche-intelligence-v2

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @2d-games

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

references

references/n/a

examples

examples/n/a

scripts

scripts/n/a

agents

agents/n/a

assets

assets/n/a

• README.md

Imported Reference Notes

Imported: User Rights (GDPR)

Ensure these are implementable from day one:

Imported: Deep Dive: Why It Matters

Data minimization — Less data = less breach impact, lower storage cost, simpler compliance. Each field is a liability.

Purpose limitation — Reusing data without consent is illegal under GDPR. Document purpose in schema or metadata.

Retention — Indefinite storage increases risk and violates GDPR. Define

retention_days

Logging — Logs often leak PII. Redact emails, IDs, tokens. Use structured logging with allowlists.

Third parties — Every SDK (analytics, crash reporting, ads) may send data elsewhere. Audit dependencies; require consent before loading.

Imported: Common Pitfalls

Imported: Third-Party Audit

Before adding a dependency that touches user data:

• What data does it collect or receive?
• Where does it send data (servers, countries)?
• Is it loaded before or after user consent?
• Can we disable it if user opts out?
• Does their privacy policy align with ours?

Imported: Implementation Checklist

When building a feature that touches user data:

• Is this data necessary? Can we achieve the goal with less?
• Do we have explicit consent for this use?
• Is it encrypted (at rest and in transit)?
• Do we have a retention/deletion policy?
• Can the user export or delete their data?
• Are third-party services disclosed and consented?
• Are logs free of PII?
• Are backups included in retention policy?

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.