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-agentic-aigit 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-agentic-ai)<a href="https://agentmods.dev/skills/tuanductran/hr-skills/hr-agentic-ai"><img src="https://agentmods.dev/badge/skills/tuanductran/hr-skills/hr-agentic-ai/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-agentic-ai"><img src="https://agentmods.dev/badge/skills/tuanductran/hr-skills/hr-agentic-ai.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 | $0.00085 | $0.01096 |
| Opus 5 | $0.00043 | $0.00548 |
| Sonnet 5 | $0.00017 | $0.00219 |
| Haiku 4.5 | $0.00009 | $0.00110 |
Grade A, and why
hr-agentic-ai 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.
Agentic AI in HR
Understand, evaluate, and govern agentic AI systems in HR — from autonomous recruiting and onboarding agents to multi-agent HR workflows, human oversight design, and governance frameworks that ensure agentic AI operates safely and accountably in people-sensitive HR contexts.
Supported tasks
- Understanding how agentic AI differs from traditional AI tools in HR contexts
- Evaluating use cases where agentic AI adds genuine HR value
- Designing human oversight frameworks for autonomous HR agents
- Governing agentic AI deployments in sensitive HR processes
- Assessing risks of autonomous AI acting without human review in HR
- Designing HR multi-agent workflows with appropriate checkpoints
- Building employee transparency frameworks for AI agent interactions
- Evaluating vendor agentic AI products for HR applications
- Designing agentic AI pilots with staged autonomy and rollback plans
- Training HR teams to work effectively alongside AI agents
- Monitoring agentic AI behavior and outcomes in production HR systems
- Building accountability frameworks for decisions made by AI agents
Key prompts
Agentic AI fundamentals for HR
- "Explain how agentic AI differs from traditional AI tools in an HR context and what new capabilities it introduces."
- "What HR processes are most suitable for agentic AI automation and why?"
- "What are the key risks of deploying autonomous AI agents in HR that involve employee data or decisions?"
- "How do we evaluate whether an agentic AI system is ready for deployment in [recruiting / onboarding / HR operations]?"
- "What questions should HR leaders ask AI vendors claiming their product has agentic capabilities?"
HR use case design
- "Design an agentic AI workflow for [recruiting screening / onboarding task sequences / HR inquiry resolution] with defined autonomy boundaries."
- "What tasks within [HR process] should an AI agent handle autonomously versus escalate to a human HR professional?"
- "Design a recruiting AI agent workflow that manages [sourcing / screening / scheduling] with human review at [defined checkpoints]."
- "How do we design an onboarding agent that guides new hires through pre-boarding tasks without replacing human connection?"
- "What agentic AI use cases in HR delivery would produce the highest efficiency gains with the lowest governance risk?"
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 · 85 tokens per session scan A ad88ce86466d
hr-agentic-ai is a skill published in the GitHub repository tuanductran/hr-skills (57 stars, last pushed yesterday), licensed MIT. It adds 85 tokens to every session and 1,096 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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