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-dpiagit 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-dpia)<a href="https://agentmods.dev/skills/mukul975/privacy-data-protection-skills/ai-dpia"><img src="https://agentmods.dev/badge/skills/mukul975/privacy-data-protection-skills/ai-dpia/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-dpia"><img src="https://agentmods.dev/badge/skills/mukul975/privacy-data-protection-skills/ai-dpia.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 | $0.00075 | $0.02782 |
| Opus 5.5 | $0.00030 | $0.01113 |
| Sonnet 5.5 | $0.00015 | $0.00556 |
| Haiku 4.5 | $0.00007 | $0.00278 |
Grade A, and why
ai-dpia 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- ai-dpia — 100% identical, 19 lines differ
How it starts
The opening of the file, as written. The whole thing — 208 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Protection Impact Assessment for AI/ML Systems
Overview
AI and ML systems present unique privacy challenges that traditional DPIA methodologies fail to adequately address. The EDPB Guidelines 04/2025 on processing personal data through AI systems establish a specialized framework that supplements the general DPIA requirements of GDPR Article 35 and WP248rev.01. AI-specific DPIAs must evaluate the entire ML pipeline — from training data collection through model deployment and inference — assessing risks that emerge from statistical learning, emergent model behaviours, and the opacity of algorithmic decision-making. This skill implements the EDPB's AI-specific DPIA methodology integrated with the EU AI Act risk classification framework.
AI-Specific DPIA Triggers
Mandatory DPIA Triggers for AI Systems
All AI processing that meets any of the following criteria requires a DPIA before deployment:
| Trigger | Legal Basis | Description |
|---|---|---|
| AI-based profiling with legal effects | Art. 35(3)(a) GDPR | ML models that produce decisions with legal or similarly significant effects on natural persons (credit scoring, hiring, insurance pricing) |
| Training on special category data | Art. 35(3)(b) GDPR | Models trained on health, biometric, genetic, racial, political, religious, sexual orientation, or trade union data at scale |
| AI-powered surveillance | Art. 35(3)(c) GDPR | Computer vision, facial recognition, behavioural analytics, or anomaly detection in public spaces |
| High-risk AI systems | Art. 6 EU AI Act | Systems listed in Annex III of the AI Act (biometric identification, critical infrastructure, employment, law enforcement, migration, justice) |
| Foundation models processing personal data | EDPB Guidelines 04/2025 | LLMs and foundation models trained on datasets containing personal data, regardless of downstream use |
| Automated inference of sensitive attributes | EDPB Guidelines 04/2025 | Models that infer Art. 9 special category data from non-sensitive inputs (inferring health status from purchasing patterns) |
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 · 208 lines · 75 tokens per session scan A a15cf651429a
ai-dpia is a skill published in the GitHub repository mukul975/Privacy-Data-Protection-Skills (321 stars, last pushed 7mo ago), licensed Apache-2.0. It adds 75 tokens to every session and 2,782 once invoked, about $0.0003 per session on Opus 5.5. 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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