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-bias-special-categorygit 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-bias-special-category)<a href="https://agentmods.dev/skills/mukul975/privacy-data-protection-skills/ai-bias-special-category"><img src="https://agentmods.dev/badge/skills/mukul975/privacy-data-protection-skills/ai-bias-special-category/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-bias-special-category"><img src="https://agentmods.dev/badge/skills/mukul975/privacy-data-protection-skills/ai-bias-special-category.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 | $0.00071 | $0.02192 |
| Opus 5 | $0.00036 | $0.01096 |
| Sonnet 5 | $0.00014 | $0.00438 |
| Haiku 4.5 | $0.00007 | $0.00219 |
Grade A, and why
ai-bias-special-category 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 — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Bias Assessment for Special Category Data
Overview
AI systems can amplify, perpetuate, or introduce bias against protected groups defined by GDPR Art. 9 special categories (race, ethnicity, political opinion, religion, trade union membership, genetic data, biometric data, health, sexual orientation) and by EU equality law (gender, age, disability). The AI Act Art. 10 requires data governance practices for training data that address bias, while Art. 5 prohibits AI-based social scoring. This skill provides the methodology for detecting, measuring, and mitigating bias in AI systems that process or infer special category data, with documentation requirements meeting both GDPR and AI Act obligations.
Art. 9 Special Categories and AI Bias
Direct Processing of Special Category Data
When AI systems directly process Art. 9 data:
| Category | AI Bias Risk | Example |
|---|---|---|
| Racial or ethnic origin | Discrimination in hiring, credit, policing | CV screening penalising names associated with ethnic minorities |
| Political opinions | Political profiling, content suppression | News recommendation amplifying or suppressing political viewpoints |
| Religious beliefs | Service denial, discriminatory targeting | Insurance pricing varying by religious affiliation |
| Trade union membership | Employment discrimination | Performance scoring penalising union activity |
| Genetic data | Genetic discrimination in insurance/employment | Health insurance pricing based on genetic predisposition |
| Biometric data | Differential accuracy across demographics | Facial recognition with higher error rates for darker skin tones |
| Health data | Health-based discrimination | Hiring algorithms penalising disability or mental health history |
| Sexual orientation | Discrimination, outing | Content recommendation inadvertently revealing sexual orientation |
Proxy Inference of Special Categories
AI models frequently infer Art. 9 data from non-sensitive features:
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 · 192 lines · 71 tokens per session scan A c8c3111e6e25
ai-bias-special-category 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 71 tokens to every session and 2,192 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.
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