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-lawfulnessgit 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-lawfulness)<a href="https://agentmods.dev/skills/onfire7777/universal-ai-skills-library/ai-training-lawfulness"><img src="https://agentmods.dev/badge/skills/onfire7777/universal-ai-skills-library/ai-training-lawfulness/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-lawfulness"><img src="https://agentmods.dev/badge/skills/onfire7777/universal-ai-skills-library/ai-training-lawfulness.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 | $0.00077 | $0.02922 |
| Opus 5 | $0.00039 | $0.01461 |
| Sonnet 5 | $0.00015 | $0.00584 |
| Haiku 4.5 | $0.00008 | $0.00292 |
Grade A, and why
ai-training-lawfulness 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.
This is a copy
98% identical to ai-training-lawfulness — 19 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 — 221 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lawful Basis for AI Training Data
Overview
The processing of personal data for AI model training constitutes a distinct processing operation requiring its own lawful basis under GDPR Art. 6(1). The EDPB Guidelines 04/2025 and the coordinated ChatGPT Taskforce findings establish that AI training creates unique lawful basis challenges: the scale of data collection, the difficulty of obtaining meaningful consent for open-ended AI training purposes, the tension between legitimate interest and data subject expectations, and the complexity of determining lawfulness for web-scraped and third-party datasets. This skill provides the comprehensive lawful basis assessment framework for AI training data processing, addressing each Art. 6(1) basis as applied to ML training contexts.
Fundamental Principles
AI Training as Personal Data Processing
The EDPB has confirmed that AI model training constitutes processing of personal data under Art. 4(2) GDPR when:
- Training datasets contain personal data (directly or indirectly identifiable natural persons)
- The model is trained on data that includes personal data, even if the intent is to learn general patterns
- The resulting model retains the capability to generate or reproduce personal data from training sets
- Personal data is used in any pipeline stage: collection, cleaning, annotation, augmentation, validation, testing
The controller cannot avoid GDPR obligations by claiming the model has "learned" rather than "stored" personal data. The processing occurs at the point of training, regardless of whether the model can later reproduce specific records.
Purpose Specification for AI Training
Art. 5(1)(b) requires that personal data be collected for specified, explicit, and legitimate purposes. For AI training, this means:
- "Training an AI model" is insufficiently specific — the controller must articulate the specific capability being developed
- "Improving our services" through AI training must be disaggregated into concrete purposes
- Each purpose must be documented before training begins, not retroactively justified
- The purpose must be communicated to data subjects in privacy notices per Arts. 13-14
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 · 221 lines · 77 tokens per session scan A 0b8391400622
ai-training-lawfulness is a skill published in the GitHub repository onfire7777/universal-ai-skills-library (16 stars, last pushed yesterday), licensed MIT. It adds 77 tokens to every session and 2,922 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to ai-training-lawfulness, differing in 19 lines, and is treated as a copy.
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