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 beel-collab/presets.dev --skill privacy-by-designgit clone --depth 1 https://github.com/beel-collab/presets.devWrote 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/beel-collab/presets.dev/privacy-by-design)<a href="https://agentmods.dev/skills/beel-collab/presets.dev/privacy-by-design"><img src="https://agentmods.dev/badge/skills/beel-collab/presets.dev/privacy-by-design/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/beel-collab/presets.dev/privacy-by-design"><img src="https://agentmods.dev/badge/skills/beel-collab/presets.dev/privacy-by-design.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 | $0.00028 | $0.01673 |
| Opus 5 | $0.00014 | $0.00837 |
| Sonnet 5 | $0.00006 | $0.00335 |
| Haiku 4.5 | $0.00003 | $0.00167 |
Grade A, and why
privacy-by-design 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 5d 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.
This is a copy
97% identical to privacy-by-design — 11 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 215 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Privacy by Design
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.
When to Use This Skill
- 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
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.
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.
User Rights (GDPR)
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.
- 5d ago First seen · 215 lines · 28 tokens per session scan A bd32c610a074
privacy-by-design is a skill published in the GitHub repository beel-collab/presets.dev (2 stars, last pushed 4mo ago), licensed MIT. It adds 28 tokens to every session and 1,673 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to privacy-by-design, differing in 11 lines, and is treated as a copy.
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