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-vendor-privacy-duegit 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-vendor-privacy-due)<a href="https://agentmods.dev/skills/onfire7777/universal-ai-skills-library/ai-vendor-privacy-due"><img src="https://agentmods.dev/badge/skills/onfire7777/universal-ai-skills-library/ai-vendor-privacy-due/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-vendor-privacy-due"><img src="https://agentmods.dev/badge/skills/onfire7777/universal-ai-skills-library/ai-vendor-privacy-due.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 | $0.00072 | $0.02050 |
| Opus 5.5 | $0.00029 | $0.00820 |
| Sonnet 5.5 | $0.00014 | $0.00410 |
| Haiku 4.5 | $0.00007 | $0.00205 |
Grade A, and why
ai-vendor-privacy-due 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.
This is a copy
92% identical to ai-vendor-privacy-due — 24 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 — 168 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Vendor Privacy Due Diligence
Overview
AI services create complex controller-processor relationships that differ significantly from traditional data processing arrangements. Whether an AI vendor is a processor, joint controller, or independent controller depends on the degree of autonomy the vendor has over personal data processing — particularly regarding model training on customer data, data retention for model improvement, and the vendor's independent purposes for the data. This skill provides the framework for determining controller-processor roles in AI service relationships, conducting privacy due diligence on AI vendors, and establishing appropriate contractual protections.
Controller-Processor Determination for AI
Decision Framework
| AI Service Model | Typical Role | Key Factors | GDPR Article |
|---|---|---|---|
| SaaS AI — Customer data processed per instructions | Vendor = Processor | Vendor processes data solely on controller's instructions; no independent use | Art. 28 DPA required |
| SaaS AI — Customer data used for model training | Vendor = Joint Controller or Independent Controller | Vendor uses customer data for own model improvement beyond contracted service | Art. 26 JCA or separate controller notice |
| Embedded AI — Pre-trained model in customer infrastructure | Customer = Controller; Vendor = may be processor for support | Model runs in customer environment; vendor may access data for support/updates | Art. 28 if vendor accesses data |
| API-based AI — Customer sends data for inference | Vendor = Processor (if no data retention) or Joint Controller (if training on inputs) | Depends on whether vendor retains, uses, or trains on input data | Assessment required |
| AI Platform — Customer builds models on vendor platform | Vendor = Processor for infrastructure; Controller for platform data | Vendor provides compute; customer controls data and model | Art. 28 DPA + audit rights |
| AI Marketplace — Pre-built models with customer data | Depends on data flow | If customer data enters vendor model → joint controller assessment | Case-by-case |
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 · 168 lines · 72 tokens per session scan A 742a1ed15531
ai-vendor-privacy-due is a skill published in the GitHub repository onfire7777/universal-ai-skills-library (16 stars, last pushed 1mo ago), licensed MIT. It adds 72 tokens to every session and 2,050 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 92% identical to ai-vendor-privacy-due, differing in 24 lines, and is treated as a copy.
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