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-data-subject-rightsgit 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-data-subject-rights)<a href="https://agentmods.dev/skills/mukul975/privacy-data-protection-skills/ai-data-subject-rights"><img src="https://agentmods.dev/badge/skills/mukul975/privacy-data-protection-skills/ai-data-subject-rights/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-data-subject-rights"><img src="https://agentmods.dev/badge/skills/mukul975/privacy-data-protection-skills/ai-data-subject-rights.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 | $0.00078 | $0.01824 |
| Opus 5 | $0.00039 | $0.00912 |
| Sonnet 5 | $0.00016 | $0.00365 |
| Haiku 4.5 | $0.00008 | $0.00182 |
Grade A, and why
ai-data-subject-rights 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 13d 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 — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Subject Rights for AI Systems
Overview
AI systems create unique challenges for data subject rights exercise. Traditional rights mechanisms designed for structured databases do not map directly to ML model architectures where personal data is encoded in model weights, reproduced in model outputs, or used in opaque decision processes. This skill provides the framework for implementing each GDPR right (Arts. 15-22) and the AI Act Art. 86 right to explanation in the context of AI processing, addressing both training-time and inference-time rights.
Rights Framework for AI
Right of Access (Art. 15)
| AI Context | Obligation | Implementation |
|---|---|---|
| Training data contribution | Confirm whether data subject's data was in training set; provide copy if feasible | Training data catalogue indexed by data subject identifier; membership query |
| AI inference inputs | Provide data used as input to AI decision | Log inference inputs with data subject linkage |
| AI inference outputs | Provide AI decision/score/classification affecting data subject | Decision logging with data subject ID |
| Logic explanation | Art. 15(1)(h): meaningful information about logic of automated decisions | SHAP/LIME explanation on request or system-level explanation |
| Training data source | Art. 14(2)(f): source of data if not collected from data subject | Training data provenance documentation |
Technical Challenges:
- Identifying specific records in massive training datasets
- Determining if a data subject's data is in the training set without running membership inference
- Providing meaningful logic explanation for complex models
Right to Rectification (Art. 16)
| AI Context | Obligation | Implementation |
|---|---|---|
| Training data correction | Correct inaccurate data in training dataset | Update training data; assess if model retraining needed |
| Model output correction | Correct inaccurate AI outputs about a data subject | Output correction mechanism; flag in decision system |
| Inference input correction | Correct data used as inference input | Update input data; re-run inference |
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.
- 13d ago First seen · 159 lines · 78 tokens per session scan A a7888667bfa6
ai-data-subject-rights 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 78 tokens to every session and 1,824 once invoked, about $0.0004 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.
Other skills, from other repositories
privacy-compliance
Comprehensive global privacy compliance agent skill covering GDPR, CCPA/CPRA, HIPAA Privacy Rule, EU AI Act, LGPD, cross-border data transfer mechanisms (SCCs, BCRs, EU-US DPF), PII identification and classification, data minimization, consent management, privacy-by-design patterns, DPIA workflows, data subject access…
fedramp
Expert guidance for FedRAMP certification and compliance under CR26 (FedRAMP Consolidated Rules for 2026). Use this skill whenever a user asks about FedRAMP authorization, ATO (Authority to Operate), cloud security for federal government, NIST SP 800-53 controls, CSP compliance, or any of the core FedRAMP document…
gdpr-compliance
Expert GDPR compliance assistant covering all four core workflows: (1) auditing code and systems for GDPR violations, (2) drafting GDPR-compliant documents such as privacy policies, Data Processing Agreements (DPAs), and consent notices, (3) answering GDPR compliance questions with authoritative article citations, and…
iso42001
Expert ISO 42001 AI Management System (AIMS) compliance advisor. Use this skill whenever a user asks about ISO/IEC 42001:2023, AI governance, AI management systems, AI risk assessment, AI system impact assessment, Annex A controls for AI, Statement of Applicability for AI systems, AI policy, responsible AI, AI…
eu-cra
Expert EU Cyber Resilience Act (CRA) advisor for Regulation (EU) 2024/2847 — mandatory cybersecurity and vulnerability handling requirements for all products with digital elements (PDEs) sold in the EU. Use this skill for gap analysis, product classification (Default / Class I / Class II), conformity assessment route…
nist-800-53
NIST SP 800-53 Rev 5 compliance advisor — all 20 control families (AC, AT, AU, CA, CM, CP, IA, IR, MA, MP, PE, PL, PM, PS, PT, RA, SA, SC, SI, SR), Low/Moderate/High baseline selection, FIPS 199/200 system categorization, control tailoring and overlays, privacy controls (PT family), supply chain risk management (SR…