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-privacy-assessmentgit 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-privacy-assessment)<a href="https://agentmods.dev/skills/onfire7777/universal-ai-skills-library/ai-privacy-assessment"><img src="https://agentmods.dev/badge/skills/onfire7777/universal-ai-skills-library/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/onfire7777/universal-ai-skills-library/ai-privacy-assessment"><img src="https://agentmods.dev/badge/skills/onfire7777/universal-ai-skills-library/ai-privacy-assessment.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 | $0.00109 | $0.03680 |
| Opus 5.5 | $0.00044 | $0.01472 |
| Sonnet 5.5 | $0.00022 | $0.00736 |
| Haiku 4.5 | $0.00011 | $0.00368 |
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.
This is a copy
89% identical to ai-privacy-assessment — 18 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 — 226 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 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 · 226 lines · 109 tokens per session scan A 7252f7d9600e
ai-privacy-assessment is a skill published in the GitHub repository onfire7777/universal-ai-skills-library (16 stars, last pushed 1mo ago), licensed MIT. It adds 109 tokens to every session and 3,680 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 89% identical to ai-privacy-assessment, differing in 18 lines, and is treated as a copy.
Other skills, from other repositories
boldsign-automation
Automate Boldsign tasks via Rube MCP (Composio). Always search tools first for current schemas.
atmos-aws-compliance
AWS compliance commands in Atmos: atmos aws compliance report, Security Hub standards, CIS AWS, PCI DSS, SOC2, HIPAA, NIST, report formats, AI summaries.
x-scorecard
OpenSSF Scorecard for assessing open source project security. Check security best practices and compliance. Dependency: This is an x-cmd module. Install x-cmd first (see x-cmd skill for installation options). see x-cmd skill for installation.
wttr-weather
Weather via wttr.in GET (no key). Use for weather/forecast; prefer os.http.request.
jk
Manage Jenkins controllers with jk, including jobs, runs, logs, artifacts, credentials, nodes, queues, and plugins.
ground-state
Before starting any non-trivial implementation, run a pre-flight reconnaissance pass to triangulate git state, project infrastructure, and prior-session memory. Auto-assembles a verified grounding preamble — a session-scoped artifact the orchestrator pastes verbatim into every subsequent sub-agent brief — eliminating…