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 HermeticOrmus/LibreSecOps-Claude-Code --skill data-protection-patternsgit clone --depth 1 https://github.com/HermeticOrmus/LibreSecOps-Claude-CodeWrote 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/hermeticormus/libresecops-claude-code/data-protection-patterns)<a href="https://agentmods.dev/skills/hermeticormus/libresecops-claude-code/data-protection-patterns"><img src="https://agentmods.dev/badge/skills/hermeticormus/libresecops-claude-code/data-protection-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/hermeticormus/libresecops-claude-code/data-protection-patterns"><img src="https://agentmods.dev/badge/skills/hermeticormus/libresecops-claude-code/data-protection-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 | $0.00054 | $0.02554 |
| Opus 5.5 | $0.00022 | $0.01022 |
| Sonnet 5.5 | $0.00011 | $0.00511 |
| Haiku 4.5 | $0.00005 | $0.00255 |
Grade A, and why
data-protection-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 12d 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 — 278 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Protection Patterns
Technical patterns for anonymization, pseudonymization, encryption, data minimization, and consent management in software systems.
Knowledge Base
Anonymization vs Pseudonymization
This distinction has significant regulatory consequences. Truly anonymous data is outside the scope of GDPR. Pseudonymous data remains personal data under GDPR, just with reduced risk.
Anonymization (irreversible -- removes data from regulation scope):
- No one, including the data controller, can re-identify individuals
- Must resist: singling out, linkability, and inference attacks
- Techniques: aggregation, generalization, suppression, noise addition
- Test: Could this data be combined with any other reasonably available dataset to identify an individual?
Pseudonymization (reversible -- reduces risk but remains personal data):
- The data controller can re-identify using a separately stored key/mapping
- Reduces risk of unauthorized re-identification
- Techniques: tokenization, hashing with salt, encryption
- Benefit: Reduced obligations in some GDPR contexts (Article 6(4)(e), Article 32, Recital 29)
Anonymization Techniques
k-Anonymity: Each record is indistinguishable from at least k-1 other records with respect to quasi-identifiers.
# Before (k=1, each record unique on age+zip)
| Age | Zip | Diagnosis |
|-----|-------|-----------|
| 28 | 10001 | Flu |
| 29 | 10002 | Diabetes |
| 35 | 10005 | Flu |
# After k-anonymity (k=2, generalize age and zip)
| Age | Zip | Diagnosis |
|-------|-------|-----------|
| 25-30 | 1000* | Flu |
| 25-30 | 1000* | Diabetes |
| 35-40 | 1000* | Flu |
Limitation: k-anonymity does not protect against attribute disclosure if all records in a group share the same sensitive value.
l-Diversity: Extends k-anonymity by requiring that each equivalence class has at least l well-represented values for the sensitive attribute.
t-Closeness: Extends l-diversity by requiring that the distribution of the sensitive attribute within each equivalence class is close to the distribution in the overall dataset.
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.
- 12d ago Changed · +5 lines · +54 tokens per session 87e16c06c0ae
- 1mo ago First seen · 273 lines · 0 tokens per session scan A c5ce97af853c
data-protection-patterns is a skill published in the GitHub repository HermeticOrmus/LibreSecOps-Claude-Code (4 stars, last pushed 9d ago), licensed MIT. It adds 54 tokens to every session and 2,554 once invoked, about $0.0002 per session on Opus 5.5. 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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