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 ThomasMoreAI/legal-skills-open --skill ai-privacy-assessmentgit clone --depth 1 https://github.com/ThomasMoreAI/legal-skills-openWrote 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/thomasmoreai/legal-skills-open/ai-privacy-assessment)<a href="https://agentmods.dev/skills/thomasmoreai/legal-skills-open/ai-privacy-assessment"><img src="https://agentmods.dev/badge/skills/thomasmoreai/legal-skills-open/ai-privacy-assessment/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/thomasmoreai/legal-skills-open/ai-privacy-assessment"><img src="https://agentmods.dev/badge/skills/thomasmoreai/legal-skills-open/ai-privacy-assessment.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 215 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- low Excessive Agency · line 52 Skill's behavior or capabilities extend beyond its stated purpose. Scope creep allows an agent to perform actions unrelated to its documented functionality, increasing the attack surface.Fix: Limit the skill's scope to its documented purpose. Remove instructions that enable the agent to perform actions outside its stated functionality.
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 | $0.00109 | $0.03695 |
| Opus 5.5 | $0.00044 | $0.01478 |
| Sonnet 5.5 | $0.00022 | $0.00739 |
| Haiku 4.5 | $0.00011 | $0.00369 |
Grade A, and why
ai-privacy-assessment 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-privacy-assessment — 89% identical, 18 lines differ
How it starts
The opening of the file, as written. The whole thing — 228 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Conducting AI System Privacy Assessment
Overview
AI systems that process personal data require a combined privacy and conformity assessment addressing both GDPR obligations and the EU AI Act (Regulation 2024/1689). This skill integrates the GDPR Art. 35 DPIA framework with AI-specific risk assessment, encompassing training data lawfulness, Art. 22 automated decision-making implications, algorithmic fairness, and the NIST AI Risk Management Framework MAP function. The assessment methodology draws from the EDPB-EDPS Joint Opinion 5/2021 on the AI Act proposal and subsequent EDPB Guidelines 06/2025 on AI and data protection.
Legal Framework
GDPR Provisions Applicable to AI
| Provision | Application to AI Systems |
|---|---|
| Art. 5(1)(a) — Lawfulness, fairness, transparency | AI processing must have a lawful basis; the logic of AI decisions must be explainable to data subjects |
| Art. 5(1)(b) — Purpose limitation | Training data collected for one purpose cannot be used to train AI models for an incompatible purpose without further lawful basis |
| Art. 5(1)(c) — Data minimisation | AI models should not require more personal data than necessary; synthetic data and anonymisation should be considered |
| Art. 5(1)(d) — Accuracy | AI outputs affecting individuals must be accurate; model drift must be monitored |
| Art. 6(1) — Lawful basis | Each stage of AI processing (data collection, model training, inference, output use) requires a lawful basis |
| Art. 9 — Special categories | Training on health, biometric, genetic, racial, political, religious, sexual orientation, or trade union data requires an Art. 9(2) exemption |
| Art. 13-14 — Transparency | Privacy notices must disclose the existence of automated decision-making, meaningful information about the logic involved, and the significance and envisaged consequences |
| Art. 22 — Automated decision-making | Data subjects have the right not to be subject to decisions based solely on automated processing that produce legal effects or similarly significantly affect them, with exceptions under Art. 22(2) |
| Art. 25 — Data protection by design | AI systems must embed privacy protections from the design phase: privacy-preserving ML techniques, differential privacy, federated learning |
| Art. 35 — DPIA | AI systems meeting EDPB WP248rev.01 criteria (evaluation/scoring, automated decision-making, innovative technology) require a DPIA |
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 · 228 lines · 109 tokens per session scan A b2b1e8d9d0fe
ai-privacy-assessment is a skill published in the GitHub repository ThomasMoreAI/legal-skills-open (95 stars, last pushed 15d ago), licensed Apache-2.0. It adds 109 tokens to every session and 3,695 once invoked, about $0.0004 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 2026-09-03.
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