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 tuanductran/hr-skills --skill hr-ai-ethicsgit clone --depth 1 https://github.com/tuanductran/hr-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/tuanductran/hr-skills/hr-ai-ethics)<a href="https://agentmods.dev/skills/tuanductran/hr-skills/hr-ai-ethics"><img src="https://agentmods.dev/badge/skills/tuanductran/hr-skills/hr-ai-ethics/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/tuanductran/hr-skills/hr-ai-ethics"><img src="https://agentmods.dev/badge/skills/tuanductran/hr-skills/hr-ai-ethics.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00086 | $0.01044 |
| Opus 5 | $0.00043 | $0.00522 |
| Sonnet 5 | $0.00017 | $0.00209 |
| Haiku 4.5 | $0.00009 | $0.00104 |
Grade A, and why
hr-ai-ethics 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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI ethics in HR
Govern the ethical use of artificial intelligence in HR processes — from assessing algorithmic fairness and auditing AI tools for bias to designing ethical AI policies, building accountability frameworks, and ensuring AI-driven HR decisions are explainable and fair.
Supported tasks
- Auditing AI tools used in hiring and talent management for bias
- Designing ethical AI use policies for HR processes
- Assessing algorithmic fairness in selection, promotion, and performance tools
- Building AI transparency and explainability standards for HR
- Designing accountability frameworks for AI-assisted HR decisions
- Evaluating vendor AI tools against ethical standards before adoption
- Training HR teams on identifying and addressing AI bias
- Designing ethical AI governance structures for HR
- Building employee disclosure and consent practices for AI in HR
- Monitoring AI tools for emerging fairness and accuracy issues
- Connecting AI ethics to compliance with anti-discrimination law
- Advising on ethical implications of AI in performance monitoring
Key prompts
AI bias and fairness
- "Design an audit process to assess whether our [AI screening tool / performance algorithm] produces biased outcomes against [protected groups]."
- "What statistical methods should we use to test for disparate impact in AI-assisted [hiring / promotion / performance] decisions?"
- "How do we interpret an AI vendor's fairness claims and determine whether they meet our standards?"
- "What does 'algorithmic fairness' mean in practice for [hiring / performance / succession] decisions and which fairness definition should we use?"
- "Design a bias red-teaming exercise for our [AI screening / assessment] tool."
- "How do we identify proxy variables in AI models that could produce discriminatory outcomes even without using protected characteristics directly?"
AI ethics policy design
- "Write an ethical AI use policy for HR covering permitted uses, prohibited uses, transparency requirements, and human oversight."
- "What principles should govern our use of AI in [hiring / performance management / monitoring / benefits]?"
- "Design a vendor AI ethics assessment checklist for evaluating HR technology providers."
- "How do we build ethical AI standards that are specific enough to be enforceable and flexible enough to keep pace with technology?"
- "Write an employee disclosure notice explaining how AI is used in [our hiring process / performance reviews]."
What ships with it
3 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 · 76 lines · 86 tokens per session scan A 86167cbacb96
hr-ai-ethics is a skill published in the GitHub repository tuanductran/hr-skills (57 stars, last pushed yesterday), licensed MIT. It adds 86 tokens to every session and 1,044 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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