privacy-check

name: privacy-check description: "Use to assess Privacy by Design compliance and GDPR/data protection alignment for a feature or system." metadata: instruction_budget: "37" framework_dependency: "mycelium" framework_dependency_note: "This skill is designed to run within the Mycelium framework (https://github.com/haabe/mycelium). Standalone use will skip the canvas state, theory gates, and harness behavior the skill assumes. Install: /plugin install mycelium@haabe/mycelium."

Privacy Check Skill

Privacy by Design assessment.

Workflow

7 Foundational Principles (Cavoukian)

1. Proactive not Reactive: Are privacy measures built in from the start?

   • Privacy considered in design phase, not bolted on
   • Risks identified before implementation

2. Privacy as Default: Is the most private option the default?

   • Data collection opt-in, not opt-out
   • Minimum data collected by default
   • Sharing disabled by default

3. Privacy Embedded in Design: Is privacy integral to the system?

   • Privacy controls are core features, not add-ons
   • Architecture supports data minimization

4. Positive-Sum, not Zero-Sum (originally "Full Functionality"): Privacy without trade-offs?

   • Privacy features don't degrade user experience
   • Not a false choice between privacy and functionality
   • Avoid false dichotomies: privacy vs. security, privacy vs. business value

5. End-to-End Security: Data protected throughout its lifecycle?

   • Encryption at rest and in transit
   • Secure deletion when no longer needed
   • Access controls throughout the data lifecycle

6. Visibility and Transparency: Is data processing transparent?

   • Users know what data is collected and why
   • Processing purposes documented and communicated
   • Third-party sharing disclosed

7. Respect for User Privacy: Are user interests centered?

   • Users can access their data
   • Users can correct their data
   • Users can delete their data
   • Consent is informed, specific, and revocable

Data Protection Assessment

• What data is collected? List all personal data fields.
• Why? Lawful basis for each data element.
• How long? Retention period for each data type.
• Who accesses it? List all parties with access.
• Where is it stored? Data residency and cross-border transfers.
• How is it protected? Encryption, access control, monitoring.
• What if breached? Incident response plan exists?

Output

## Privacy Assessment: [Feature/System]

### PbD Principles
| Principle | Status | Notes |
|-----------|--------|-------|
| Proactive | Pass/Fail | ... |
| Default privacy | Pass/Fail | ... |
| Embedded | Pass/Fail | ... |
| Full functionality | Pass/Fail | ... |
| End-to-end security | Pass/Fail | ... |
| Transparency | Pass/Fail | ... |
| User respect | Pass/Fail | ... |

### Data Inventory
| Data | Purpose | Basis | Retention | Protection |
|------|---------|-------|-----------|-----------|
| ... | ... | ... | ... | ... |

### Risks and Recommendations
1. [risk and recommended action]

Decision Log (MANDATORY per G-P4)

APPEND a ### Privacy Assessment entry to .claude/harness/decision-log.md with: principles assessed, data flows identified, risks found, GDPR compliance status.

Theory Citations

• Cavoukian: Privacy by Design (7 principles)
• GDPR: Data protection regulation