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-transparency-reqsgit 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-transparency-reqs)<a href="https://agentmods.dev/skills/onfire7777/universal-ai-skills-library/ai-transparency-reqs"><img src="https://agentmods.dev/badge/skills/onfire7777/universal-ai-skills-library/ai-transparency-reqs/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-transparency-reqs"><img src="https://agentmods.dev/badge/skills/onfire7777/universal-ai-skills-library/ai-transparency-reqs.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 | $0.00070 | $0.02428 |
| Opus 5 | $0.00035 | $0.01214 |
| Sonnet 5 | $0.00014 | $0.00486 |
| Haiku 4.5 | $0.00007 | $0.00243 |
Grade A, and why
ai-transparency-reqs 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 8d 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
95% identical to ai-transparency-reqs — 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 — 189 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Transparency Requirements
Overview
AI transparency operates at the intersection of two regulatory frameworks: the GDPR's data subject information rights (Arts. 13-14) and the EU AI Act's transparency obligations (Arts. 13-14, 50). Together they require controllers and deployers to provide meaningful, accessible information about AI system capabilities, limitations, decision logic, and personal data processing. This skill implements the combined transparency framework, addressing both the technical explainability challenge of complex ML models and the legal obligation to communicate AI processing in plain language to affected individuals.
GDPR Transparency for AI Systems
Art. 13-14 Information Requirements Applied to AI
When personal data is processed by AI systems, data subjects must receive:
| Information Element | GDPR Article | AI-Specific Application |
|---|---|---|
| Purposes of processing | Art. 13(1)(c) / 14(1)(c) | Specific AI use case, not generic "service improvement" |
| Lawful basis | Art. 13(1)(c) / 14(1)(c) | The basis for AI training and for AI inference separately |
| Legitimate interest | Art. 13(1)(d) / 14(2)(b) | The specific interest served by AI processing |
| Recipients | Art. 13(1)(e) / 14(1)(e) | AI infrastructure providers, model hosting services |
| International transfers | Art. 13(1)(f) / 14(1)(f) | Where AI processing occurs (training and inference locations) |
| Retention period | Art. 13(2)(a) / 14(2)(a) | Training data retention, inference log retention, model lifecycle |
| Data subject rights | Art. 13(2)(b) / 14(2)(c) | Including AI-specific rights: explanation, contestation, human review |
| Automated decision-making | Art. 13(2)(f) / 14(2)(g) | Meaningful information about logic, significance, and envisaged consequences |
| Source of data | Art. 14(2)(f) | Training data sources (categories, not necessarily individual sources) |
Art. 13(2)(f) / 14(2)(g) — Meaningful Information About Automated Logic
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.
- 8d ago First seen · 189 lines · 70 tokens per session scan A 518fe0bda5e8
ai-transparency-reqs is a skill published in the GitHub repository onfire7777/universal-ai-skills-library (16 stars, last pushed 1mo ago), licensed MIT. It adds 70 tokens to every session and 2,428 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to ai-transparency-reqs, differing in 19 lines, and is treated as a copy.
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