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-deployment-checklistgit 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-deployment-checklist)<a href="https://agentmods.dev/skills/mukul975/privacy-data-protection-skills/ai-deployment-checklist"><img src="https://agentmods.dev/badge/skills/mukul975/privacy-data-protection-skills/ai-deployment-checklist/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-deployment-checklist"><img src="https://agentmods.dev/badge/skills/mukul975/privacy-data-protection-skills/ai-deployment-checklist.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 | $0.00060 | $0.01688 |
| Opus 5 | $0.00030 | $0.00844 |
| Sonnet 5 | $0.00012 | $0.00338 |
| Haiku 4.5 | $0.00006 | $0.00169 |
Grade A, and why
ai-deployment-checklist 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 — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI System Pre-Deployment Privacy Checklist
Overview
Deploying an AI system that processes personal data requires verification of privacy compliance across multiple dimensions before the system goes live. This checklist serves as a compliance gate in the Cerebrum AI Labs ML deployment pipeline. No AI system may be deployed to production until all mandatory items are verified and signed off by the Data Protection Officer (DPO). The checklist is structured around GDPR requirements, the EU AI Act obligations (for high-risk systems), and internal governance standards.
Pre-Deployment Compliance Gate
Gate 1: Legal Basis and DPIA
| Check | Requirement | Status | Evidence |
|---|---|---|---|
| Lawful basis documented | Art. 6(1) basis identified and recorded for all personal data processing | Required | LIA or consent records |
| Special categories assessed | Art. 9 data identified; explicit consent or Art. 9(2) exception documented | Required | Data classification report |
| DPIA completed | Art. 35 DPIA completed for high-risk processing (profiling, systematic monitoring, large-scale special categories) | Required if applicable | DPIA document signed by DPO |
| DPIA risks mitigated | All high/critical risks from DPIA have documented mitigations | Required | Risk treatment plan |
| Prior consultation | Art. 36 consultation with supervisory authority if residual risk remains high | Required if applicable | Consultation record |
| Legitimate interest assessment | If relying on Art. 6(1)(f), LIA balancing test completed | Required if LI basis | LIA document |
Gate 2: Transparency and Information
| Check | Requirement | Status | Evidence |
|---|---|---|---|
| Privacy notice updated | Art. 13-14 information includes AI processing details | Required | Updated privacy notice |
| Logic described | "Meaningful information about the logic involved" documented for data subjects | Required for automated decisions | Explanation document |
| Significance disclosed | Envisaged consequences of AI processing disclosed | Required for automated decisions | Privacy notice section |
| Profiling disclosed | If system profiles individuals, this is disclosed in privacy notice | Required if profiling | Privacy notice section |
| AI Act transparency | Art. 52 transparency obligations met (if applicable): inform that they are interacting with AI | Required for AI Act | User interface disclosure |
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 · 120 lines · 60 tokens per session scan A 4ddf4ad57a65
ai-deployment-checklist 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 60 tokens to every session and 1,688 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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