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 mukul975/Privacy-Data-Protection-Skills --skill ai-privacy-inferencegit clone --depth 1 https://github.com/mukul975/Privacy-Data-Protection-SkillsWrote 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/mukul975/privacy-data-protection-skills/ai-privacy-inference)<a href="https://agentmods.dev/skills/mukul975/privacy-data-protection-skills/ai-privacy-inference"><img src="https://agentmods.dev/badge/skills/mukul975/privacy-data-protection-skills/ai-privacy-inference/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/mukul975/privacy-data-protection-skills/ai-privacy-inference"><img src="https://agentmods.dev/badge/skills/mukul975/privacy-data-protection-skills/ai-privacy-inference.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 | $0.00057 | $0.02908 |
| Opus 5 | $0.00028 | $0.01454 |
| Sonnet 5 | $0.00011 | $0.00582 |
| Haiku 4.5 | $0.00006 | $0.00291 |
Grade A, and why
ai-privacy-inference 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 12d 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 — 195 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Privacy Inference and Derived Data
Overview
AI systems routinely generate inferences about individuals — predictions about creditworthiness, health risks, personality traits, political opinions, or behavioural patterns that were never directly provided by the data subject. These AI-derived inferences raise critical privacy questions: Are inferences personal data? When does inference become profiling under GDPR Article 22? What accuracy obligations apply to AI predictions? Can data subjects access, rectify, or object to inferences drawn about them? The CJEU, EDPB, and national DPAs have progressively clarified that inferences are personal data when they relate to an identified or identifiable person, and that GDPR rights extend to derived and inferred data. Cerebrum AI Labs must classify, govern, and provide transparency over all inferences its AI systems generate about individuals.
Legal Framework for AI Inferences
When Inferences Are Personal Data
| Criterion | Analysis | Example |
|---|---|---|
| Relates to an identified person | Inference is linked to a specific customer record or user profile | "Customer C-12345 has 78% churn probability" |
| Relates to an identifiable person | Inference can be linked to a person through combination with other data | "User with session token X-789 is likely aged 25-34" |
| Used to evaluate a person | Inference is used to assess, classify, or make decisions about someone | Credit score derived from transaction patterns |
| Has impact on a person | Inference affects how the person is treated or what options are available | Insurance premium adjusted based on predicted health risk |
CJEU C-434/16 (Nowak, 2017): Personal data includes "any information" relating to a data subject — this encompasses opinions, assessments, and inferences, not only factual data directly provided by the individual.
EDPB Guidelines 8/2020 on Targeting of Social Media Users: Inferred data (data created by the controller through observation or derivation) constitutes personal data and is subject to the full scope of GDPR rights.
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.
- 12d ago First seen · 195 lines · 57 tokens per session scan A d64295961252
ai-privacy-inference is a skill published in the GitHub repository mukul975/Privacy-Data-Protection-Skills (272 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 57 tokens to every session and 2,908 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 2026-08-30.
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