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 anonymization-alternativegit 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/anonymization-alternative)<a href="https://agentmods.dev/skills/onfire7777/universal-ai-skills-library/anonymization-alternative"><img src="https://agentmods.dev/badge/skills/onfire7777/universal-ai-skills-library/anonymization-alternative/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/anonymization-alternative"><img src="https://agentmods.dev/badge/skills/onfire7777/universal-ai-skills-library/anonymization-alternative.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 | $0.00080 | $0.03424 |
| Opus 5.5 | $0.00032 | $0.01370 |
| Sonnet 5.5 | $0.00016 | $0.00685 |
| Haiku 4.5 | $0.00008 | $0.00342 |
Grade A, and why
anonymization-alternative 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 1mo 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 — 229 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Anonymization as Retention Alternative
Overview
Anonymization transforms personal data into a form that no longer identifies or can reasonably be used to identify a natural person. Under GDPR Recital 26, truly anonymized data falls outside the scope of the regulation, meaning it can be retained indefinitely without a legal basis, without data subject rights applying, and without counting toward retention period obligations. However, achieving genuine anonymization — as opposed to mere pseudonymization — requires rigorous application of techniques validated against re-identification risk. This skill provides the assessment framework, implementation techniques, and validation methods for using anonymization as an alternative to deletion when retention of aggregate or statistical data serves a legitimate purpose.
Legal Foundation
GDPR Recital 26 — Anonymized Data Outside GDPR Scope
"The principles of data protection should therefore not apply to anonymous information, namely information which does not relate to an identified or identifiable natural person or to personal data rendered anonymous in such a manner that the data subject is not or no longer identifiable. This Regulation does not therefore concern the processing of such anonymous information, including for statistical or research purposes."
The critical test: whether the data subject is identifiable, taking into account "all the means reasonably likely to be used" either by the controller or "any other person" to identify the natural person.
Article 29 Working Party Opinion 05/2014 on Anonymization Techniques (WP216)
Adopted 10 April 2014, this Opinion establishes that effective anonymization must prevent:
- Singling out: Isolating some or all records which identify an individual in the dataset.
- Linkability: Linking at least two records concerning the same data subject (within the same dataset or between two separate datasets).
- Inference: Deducing, with significant probability, the value of an attribute from the values of a set of other attributes.
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.
- 1mo ago First seen · 229 lines · 80 tokens per session scan A 24dba4d6fd77
anonymization-alternative is a skill published in the GitHub repository onfire7777/universal-ai-skills-library (16 stars, last pushed 1mo ago), licensed MIT. It adds 80 tokens to every session and 3,424 once invoked, about $0.0003 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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