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.
npx skills add onfire7777/universal-ai-skills-library --skill applying-privacy-design-patternsgit clone --depth 1 https://github.com/onfire7777/universal-ai-skills-libraryWrote 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.
[](https://agentmods.dev/skills/onfire7777/universal-ai-skills-library/applying-privacy-design-patterns)<a href="https://agentmods.dev/skills/onfire7777/universal-ai-skills-library/applying-privacy-design-patterns"><img src="https://agentmods.dev/badge/skills/onfire7777/universal-ai-skills-library/applying-privacy-design-patterns/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.
<a href="https://agentmods.dev/skills/onfire7777/universal-ai-skills-library/applying-privacy-design-patterns"><img src="https://agentmods.dev/badge/skills/onfire7777/universal-ai-skills-library/applying-privacy-design-patterns.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00063 | $0.02358 |
| Opus 5 | $0.00032 | $0.01179 |
| Sonnet 5 | $0.00013 | $0.00472 |
| Haiku 4.5 | $0.00006 | $0.00236 |
Grade A, and why
applying-privacy-design-patterns 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.
How it starts
The opening of the file, as written. The whole thing — 198 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Applying Privacy Design Patterns
Overview
Privacy design patterns provide reusable architectural solutions for implementing data protection principles in system design. Jaap-Henk Hoepman's framework (2014, expanded in "Privacy Design Strategies: The Eight Strategies for GDPR Compliance") defines eight privacy design strategies organized into two categories: data-oriented strategies (minimize, hide, separate, abstract) that focus on the processing of personal data itself, and process-oriented strategies (inform, control, enforce, demonstrate) that focus on the organizational processes surrounding data processing.
These patterns directly implement GDPR Article 25(1) data protection by design and map to specific GDPR principles under Article 5.
The Eight Privacy Design Patterns
Data-Oriented Strategies
1. MINIMIZE
Principle: Limit the processing of personal data as much as possible.
GDPR mapping: Article 5(1)(c) data minimization, Article 25(2) by default.
Sub-patterns:
| Sub-pattern | Description | Implementation |
|---|---|---|
| Select before collect | Determine which data is necessary before designing collection interfaces | API allowlists, form field audits |
| Exclude | Remove unnecessary data elements from collection | Schema validation rejecting non-required fields |
| Strip | Remove identifying information as soon as possible after collection | Pseudonymization at ingestion boundary |
| Destroy | Delete data as soon as the purpose is fulfilled | TTL-based automated deletion |
Prism Data Systems AG Implementation:
The customer onboarding API at Prism Data Systems AG validates incoming requests against a strict allowlist. The /api/v2/register endpoint accepts only email, display_name, and country_code. The date_of_birth field is collected only during age verification and is converted to a boolean is_age_verified within 24 hours, with the raw date destroyed.
2. HIDE
Principle: Protect personal data, or make it unlinkable or unobservable.
What ships with it
4 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.
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.
- 9d ago First seen · 198 lines · 63 tokens per session scan A c0667010f084
applying-privacy-design-patterns is a skill published in the GitHub repository onfire7777/universal-ai-skills-library (16 stars, last pushed yesterday), licensed MIT. It adds 63 tokens to every session and 2,358 once invoked, about $0.0003 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.
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