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 ai-training-data-classgit 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/ai-training-data-class)<a href="https://agentmods.dev/skills/onfire7777/universal-ai-skills-library/ai-training-data-class"><img src="https://agentmods.dev/badge/skills/onfire7777/universal-ai-skills-library/ai-training-data-class/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/ai-training-data-class"><img src="https://agentmods.dev/badge/skills/onfire7777/universal-ai-skills-library/ai-training-data-class.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 | $0.00066 | $0.02327 |
| Opus 5 | $0.00033 | $0.01163 |
| Sonnet 5 | $0.00013 | $0.00465 |
| Haiku 4.5 | $0.00007 | $0.00233 |
Grade A, and why
ai-training-data-class 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 — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Sensitive Data Classification for AI/ML Training Datasets
Overview
AI and machine learning models trained on personal data raise distinct classification challenges. Training data may contain direct personal data, inferred special categories, proxy variables for protected characteristics, and data whose consent scope does not extend to model training. The EU AI Act (Regulation (EU) 2024/1689) imposes additional requirements for high-risk AI systems, including data governance obligations under Art. 10 that intersect with GDPR classification requirements. This skill provides a framework for classifying training data, detecting bias-relevant features, documenting data provenance, and verifying consent coverage.
GDPR and AI Act Intersection
GDPR Requirements for Training Data
| GDPR Article | Application to AI Training |
|---|---|
| Art. 5(1)(b) — Purpose limitation | Training a model is a distinct processing purpose; if data was collected for customer service, using it for ML training requires a compatible purpose assessment or new lawful basis |
| Art. 5(1)(c) — Data minimisation | Training datasets must not include more personal data than necessary for the model objective |
| Art. 6 — Lawful basis | Model training requires its own lawful basis; legitimate interests (Art. 6(1)(f)) is most common, but requires LIA documentation |
| Art. 9 — Special categories | If training data contains or enables inference of special category data, Art. 9(2) condition required |
| Art. 22 — Automated decision-making | If the trained model makes decisions with legal or significant effects, additional safeguards apply |
| Art. 25 — Data protection by design | Classification of training data is a by-design measure enabling appropriate technical protections |
| Art. 35 — DPIA | High-risk AI processing (profiling, automated decision-making) requires DPIA |
EU AI Act Art. 10 — Data Governance for High-Risk AI
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 · 149 lines · 66 tokens per session scan A 4931aeee7207
ai-training-data-class is a skill published in the GitHub repository onfire7777/universal-ai-skills-library (16 stars, last pushed yesterday), licensed MIT. It adds 66 tokens to every session and 2,327 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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