magnus919_agent-skills/privacy-engineering/SKILL.md

name, description, license, compatibility, metadata

name	description	license	compatibility	metadata
privacy-engineering	Translate privacy principles and legal requirements into data-flow, lifecycle, acceptance, and verification artifacts. Map data classification, purpose, processing, access, retention, deletion, residency, and consent; define verifiable privacy acceptance criteria; and produce data-lifecycle records, retention/deletion verification plans, and privacy change reviews. Use when engineering privacy into a system, feature, or data flow — not for legal advice, jurisdiction-specific regulatory interpretation, or replacing security engineering or incident response.	MIT	No runtime dependency. Host-, platform-, and regulation-neutral methodology.	tags privacy-engineering, data-lifecycle, retention, deletion, residency, consent, data-classification, privacy-acceptance-criteria, privacy-change-review, minimization, tenant-isolation

Privacy Engineering

Translate privacy principles into engineering artifacts that are observable, testable, and verifiable. This skill does not provide legal advice and does not substitute for jurisdiction-specific regulatory interpretation; those belong to qualified legal counsel and to legal-strategy.

Disclaimer

This skill does not provide legal advice. It provides an engineering method for translating privacy requirements (whether derived from GDPR, CCPA, HIPAA, internal policy, or contractual obligations) into verifiable technical artifacts. Jurisdiction-specific regulatory interpretation must be escalated to qualified legal counsel. Do not use this skill to determine whether a specific regulatory regime applies or to interpret the legal scope of a privacy obligation.

When to use

Load this skill when the task involves engineering privacy into a system, feature, or data flow:

• Map data classification, purpose, processing activities, access patterns, retention periods, deletion workflows, residency constraints, and consent flows.
• Define privacy acceptance criteria that are testable and verifiable — not policy prose alone.
• Produce a data-lifecycle record that traces data from collection through deletion across all stores and backups.
• Design a retention/deletion verification plan with measurable success conditions (e.g., "data for user X deleted from all primary stores within Y hours of verified account closure").
• Map data flows across service boundaries, tenant boundaries, and geographic regions, identifying where PII transits or resides.
• Review a change (feature, schema, integration, AI pipeline) for privacy impact and produce a privacy change review.
• Address privacy implications of agent traces (LLM conversation logs, tool-call history) and product analytics telemetry.
• Integrate consent and revocation signals into system behavior.
• Apply data minimization and purpose limitation at the engineering level.
• Define tenant-boundary isolation requirements for multi-tenant data.

When not to use

• Legal advice or regulatory interpretation: route to legal-strategy. Privacy engineering translates legal requirements into engineering artifacts; it does not interpret the law.
• Security implementation (authN/Z, encryption at rest/transit, threat modeling, vulnerability management): route to secure-software-engineering. Privacy engineering defines what data protections are required; security engineering implements how those protections are enforced.
• Security auditing and vulnerability assessment: route to security-audit-methodology.
• Data architecture, schema design, storage selection, pipeline implementation: route to data-engineering. Privacy engineering defines retention and deletion requirements; data engineering implements the storage and pipeline mechanics.
• Incident response and live-service reliability: the skill names privacy-incident triggers and evidence requirements, but live incident command belongs to site-reliability-engineering and post-incident learning to incident-learning (same-wave skill; prose reference only).
• Production readiness evidence assembly and launch decisions: route to production-readiness. Privacy engineering produces the privacy-dimension evidence that feeds the readiness packet.
• Product analytics measurement design: route to product-analytics-and-measurement. Privacy engineering defines the privacy constraints on measurement; product analytics designs the measurement strategy within those constraints.
• Agent evaluation design and observability instrumentation: route to agent-evals-and-observability. Privacy engineering defines privacy requirements for agent traces and telemetry; agent-evals-and-observability implements the minimized, redacted collection.

Privacy dimensions (structured concerns)

Every privacy engineering engagement must address these seven dimensions. At least six must appear as structured fields (checklist items, table rows, or labeled fields) in any artifact produced; all seven must be present somewhere in the body of work.

#	Dimension	Engineering concern
1	Purpose	What is the declared purpose for collecting/processing this data? Is each processing activity tied to a specific, explicit purpose?
2	Lifecycle (retention)	What is the retention period for each data category? What triggers the retention clock? Where is the retention policy enforced?
3	Access	Who or what can access this data? Under what conditions? Is access logged and reviewed? Are access patterns consistent with the declared purpose?
4	Deletion	How is data deleted when retention expires or a deletion request is received? Is deletion verified across all stores, backups, caches, and derived datasets?
5	Tenant / isolation	How is data isolated between tenants? Are tenant-boundary violations detectable? Are cross-tenant queries prevented by default?
6	Residency	Where does data reside at rest and in transit? Are there geographic or jurisdictional constraints on data location? Are residency constraints enforced at the infrastructure level?
7	Consent	Is consent obtained before data collection? Can consent be withdrawn? Is the system behavior different pre-consent, post-consent, and post-revocation?

Working method

1. Start from purpose, not technology. For every data element, ask: why is this collected? What outcome does it serve? If the purpose cannot be stated in one sentence, the data element is suspect.

2. Classify before you collect. Assign a data classification (public, internal, confidential, restricted/PII) and a purpose label before instrumentation or storage is designed.

3. Map the full data lifecycle. Trace each data category from collection through processing, storage, access, archival, and deletion. Include caches, replicas, backups, logs, and derived datasets. A lifecycle that ends at "primary database" is incomplete.

4. Define verifiable acceptance criteria. Every privacy requirement must produce at least one acceptance criterion that is testable — a specific, measurable condition with a verification method and a pass/fail condition. "Data is handled securely" is not verifiable. "PII does not appear in analytics export" is.

5. Verify deletion, not just policy. A retention policy that is not verified through a deletion test is a policy document, not an engineering artifact. Produce a retention/deletion verification plan and exercise it.

6. Route legal interpretation to legal counsel. When a question requires interpreting the scope of a regulation, determining applicability, or resolving a legal ambiguity, escalate — do not interpret.

7. Address agent traces and product telemetry explicitly. Any system that logs LLM conversations, tool-call history, user interactions, or product analytics must have privacy controls designed before collection begins.

Agent traces and product analytics telemetry

Agent traces

Agent traces include LLM conversation logs, tool-call arguments and responses, intermediate reasoning, and agent state transitions. These traces often contain sensitive data: user prompts, system context, tool outputs, and PII that the agent accessed during a task.

Privacy engineering for agent traces requires:

• Minimization: capture only what is necessary for the declared purpose (debugging, evaluation, audit). Strip prompt content, tool arguments, and intermediate reasoning before storage unless each field is justified.
• Redaction: apply redaction before traces leave the execution boundary. Redact PII, credentials, and sensitive context. Redaction is not a post-storage filter — it must happen at collection time.
• Retention: define a trace retention period tied to a specific purpose. Debugging traces may need hours; evaluation datasets may need longer but require consent and provenance.
• Deletion: traces must be deletable. A deletion request must remove traces from all stores (primary, archive, backup) and the deletion must be verifiable.
• Consent: where traces contain user content, consent must be obtained before collection. Consent revocation must stop future collection and trigger deletion of existing traces where legally required.

Product analytics telemetry

Product analytics telemetry includes event streams, user-behavior tracking, feature-usage metrics, and session recordings. Privacy engineering defines the constraints; product-analytics-and-measurement designs the measurement strategy within those constraints.

Privacy engineering for analytics telemetry requires:

• Consent boundary: define what is measured before consent, after consent, and what is never measured. The pre-consent set must be minimal — strictly necessary for system operation.
• Minimization: collect only properties that serve a declared measurement purpose. Speculative properties ("we might need this later") are rejected.
• Aggregation threshold: metrics must not be reported when the contributing cohort falls below a minimum size that risks re-identification.
• Retention and deletion: raw event retention period must be defined. Deletion requests must cascade to derived datasets and aggregated metrics where the individual contribution is identifiable.
• Jurisdictional awareness: note when telemetry crosses regulatory boundaries. The engineering artifacts record the constraint; legal interpretation of applicability is escalated.

File map

Path	Loaded when
references/discovery-brief.md	Understanding ownership boundaries between legal, privacy, security, and data engineering
templates/data-lifecycle-record.md	Producing a full data-lifecycle record from collection through deletion
templates/privacy-acceptance-criteria.md	Defining verifiable, testable privacy acceptance criteria with verification methods and pass/fail conditions
templates/data-flow-and-access-map.md	Mapping data flows across services, tenants, and regions with access patterns
templates/retention-deletion-verification-plan.md	Designing a retention/deletion verification plan with measurable success conditions
templates/privacy-change-review.md	Reviewing a change for privacy impact and producing a privacy change review
evals/evals.json	Evaluating the skill's output quality across representative scenarios

Routing and ownership boundaries

What this skill owns

• Privacy requirements translation into engineering artifacts.
• Data lifecycle tracing (collection → processing → storage → access → deletion).
• Privacy acceptance criteria (verifiable, testable, measurable).
• Retention and deletion verification planning.
• Consent and revocation engineering.
• Tenant-boundary isolation requirements.
• Residency constraint engineering.
• Privacy change review.
• Privacy dimension of agent traces and product analytics.

What this skill does NOT own

• Legal interpretation: owned by legal-strategy. Privacy engineering consumes legal requirements as input; it does not produce them or interpret regulatory scope.
• Security implementation: owned by secure-software-engineering. Privacy engineering says "PII must be encrypted at rest"; security engineering selects the encryption scheme, manages keys, and verifies implementation.
• Security auditing: owned by security-audit-methodology.
• Data architecture and pipeline implementation: owned by data-engineering. Privacy engineering defines retention periods and deletion requirements; data engineering implements the storage, backup, and pipeline mechanics that satisfy them.
• Incident response: owned by site-reliability-engineering for live command; incident-learning (same-wave, prose only) for post-incident learning. Privacy engineering defines what constitutes a privacy incident and what evidence is required; it does not run incident command.
• Production readiness: owned by production-readiness. Privacy engineering feeds the privacy-dimension evidence into the readiness packet.
• Product analytics measurement: owned by product-analytics-and-measurement. Privacy engineering defines the privacy constraints; product analytics designs measurement within them.
• Agent evaluation and telemetry: owned by agent-evals-and-observability. Privacy engineering defines the privacy requirements for telemetry.

Feeds (prose references to skills not yet landed)

Privacy engineering produces artifacts that feed:

• agent-production-operations (M3 bundle) — privacy acceptance criteria and data-lifecycle records as production evidence.
• incident-learning (same-wave skill) — privacy incident evidence and change-review findings for post-incident learning pipeline.

Core principles

• Purpose before collection: no data element is collected without a declared, specific purpose. "It might be useful later" is not a purpose.
• Minimization by default: collect the minimum set of data properties needed to satisfy the declared purpose. Reject speculative collection.
• Deletion is a feature: every data element has a defined end of life. Deletion must be verifiable — a policy that says "data is deleted after 90 days" without a verification plan is not an engineering artifact.
• Verifiable, not aspirational: every privacy requirement produces acceptance criteria with a verification method and pass/fail condition. "Data is handled securely" fails this standard.
• Consent is a state machine: consent is obtained, recorded, honored, and revocable. System behavior must differ measurably across pre-consent, post-consent, and post-revocation states.
• Tenant isolation is a hard boundary: in multi-tenant systems, data from one tenant must never be accessible to another. Cross-tenant queries must be prevented by default, not filtered after the fact.
• Escalate jurisdictional questions: when a requirement depends on whether a specific regulation applies or how a jurisdiction interprets a privacy obligation, escalate to legal counsel. This skill does not answer those questions.