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-impact-templategit 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-impact-template)<a href="https://agentmods.dev/skills/mukul975/privacy-data-protection-skills/ai-privacy-impact-template"><img src="https://agentmods.dev/badge/skills/mukul975/privacy-data-protection-skills/ai-privacy-impact-template/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-impact-template"><img src="https://agentmods.dev/badge/skills/mukul975/privacy-data-protection-skills/ai-privacy-impact-template.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 | $0.00069 | $0.01540 |
| Opus 5 | $0.00034 | $0.00770 |
| Sonnet 5 | $0.00014 | $0.00308 |
| Haiku 4.5 | $0.00007 | $0.00154 |
Grade A, and why
ai-privacy-impact-template 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 — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Combined DPIA and AI Act Conformity Assessment Template
Overview
High-risk AI systems under the EU AI Act must undergo both a GDPR Art. 35 DPIA and an AI Act conformity assessment. Rather than conducting these as separate exercises, this skill provides an integrated template that satisfies both frameworks simultaneously. The combined assessment ensures consistency between GDPR privacy risk analysis and AI Act safety and fundamental rights evaluation, reduces duplication, and provides a single risk scoring matrix covering both regulatory dimensions. Art. 26(9) AI Act explicitly requires deployers to use DPIA results when fulfilling AI Act obligations.
Combined Risk Scoring Matrix
Risk Dimensions
| Dimension | Source | Weight |
|---|---|---|
| Privacy risk to data subjects | GDPR Art. 35(7)(c) | 30% |
| Fundamental rights impact | EU AI Act Art. 9(2)(a) | 25% |
| Accuracy and reliability risk | EU AI Act Art. 15 | 20% |
| Transparency and explainability gap | GDPR Art. 13(2)(f) + AI Act Art. 13 | 15% |
| Human oversight adequacy | GDPR Art. 22 + AI Act Art. 14 | 10% |
Scoring Scale (Per Dimension)
| Score | Level | Description |
|---|---|---|
| 1 | Minimal | Risk negligible; controls effective |
| 2 | Low | Minor risk; standard controls sufficient |
| 3 | Medium | Moderate risk; enhanced controls needed |
| 4 | High | Significant risk; intensive mitigation required |
| 5 | Critical | Severe risk; may require processing suspension |
Overall Risk Classification
| Weighted Score | Classification | Action Required |
|---|---|---|
| 1.0-1.5 | Low | Standard monitoring |
| 1.6-2.5 | Medium | Enhanced monitoring and periodic review |
| 2.6-3.5 | High | Active mitigation and DPO/Board oversight |
| 3.6-4.5 | Very High | Art. 36 prior consultation; deployment hold pending mitigation |
| 4.6-5.0 | Critical | Do not deploy; fundamental redesign required |
GDPR DPIA Requirements (Art. 35(7))
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 · 118 lines · 69 tokens per session scan A 232450895a52
ai-privacy-impact-template 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 69 tokens to every session and 1,540 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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