privacy-engineering

privacy-engineering is a skill for Claude Code, Codex from magnus919/agent-skills. It costs 98 tokens per session (3,335 once invoked), scanned A, original, MIT.

An engineering method for turning privacy requirements into testable system rules and records. It covers what data is collected, why it is used, who can access it, how long it is kept, and how it is deleted or located.

In plain words
What is it for?
Use it to map data flows, define access, retention, deletion, residency, and consent requirements, and create privacy acceptance criteria that can be checked.
Why use it?
It helps teams make privacy obligations visible and verifiable in the software instead of leaving them as vague policy statements. It also separates engineering work from legal interpretation.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to map data flows, define access, retention, deletion, residency, and consent requirements, and create privacy acceptance criteria that can be checked.

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Install with agentmods
npx agentmods add skills/magnus919/agent-skills/privacy-engineering
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add magnus919/agent-skills --skill privacy-engineering
Clone the repo
git clone --depth 1 https://github.com/magnus919/agent-skills

Made for: Claude Code, Codex.

Its marketplace also offers this one on its own, as the plugin privacy-engineering/plugin install privacy-engineering after adding the marketplace above.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for privacy-engineering

README.md
[![agentmods](https://agentmods.dev/badge/skills/magnus919/agent-skills/privacy-engineering/github.svg)](https://agentmods.dev/skills/magnus919/agent-skills/privacy-engineering)
Your own site
<a href="https://agentmods.dev/skills/magnus919/agent-skills/privacy-engineering"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/privacy-engineering/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for privacy-engineering

Your own site · 80×15
<a href="https://agentmods.dev/skills/magnus919/agent-skills/privacy-engineering"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/privacy-engineering.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 98 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,335 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00098 $0.03335
Opus 5 $0.00049 $0.01667
Sonnet 5 $0.00020 $0.00667
Haiku 4.5 $0.00010 $0.00333

Measured 9d ago against content hash cafdc626caaf, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

privacy-engineering scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

privacy-engineering/SKILL.md · 285 lines

How it starts

The opening of the file, as written. The whole thing — 285 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.

Read the full file on GitHub · 285 lines

Files

What ships with it

8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 285 lines · 98 tokens per session scan A cafdc626caaf

Subscribe to this mod's changes

privacy-engineering is a skill published in the GitHub repository magnus919/agent-skills (76 stars, last pushed yesterday), licensed MIT. It adds 98 tokens to every session and 3,335 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.