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-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-ai)<a href="https://agentmods.dev/skills/tuanductran/hr-skills/hr-ai"><img src="https://agentmods.dev/badge/skills/tuanductran/hr-skills/hr-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-ai"><img src="https://agentmods.dev/badge/skills/tuanductran/hr-skills/hr-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 | $0.00090 | $0.02177 |
| Opus 5 | $0.00045 | $0.01089 |
| Sonnet 5 | $0.00018 | $0.00435 |
| Haiku 4.5 | $0.00009 | $0.00218 |
Grade A, and why
hr-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 — 366 lines — stays where its author put it; the contents beside it link to each section on GitHub.
HR AI hiring
Comprehensive AI and Machine Learning knowledge for HR and recruiters — from understanding modern AI ecosystems and LLM workflows to evaluating AI candidates, interpreting portfolios, and improving technical hiring decisions.
Supported tasks
- Explaining AI and machine learning concepts for non-technical recruiters
- Understanding modern AI ecosystems and LLM workflows
- Screening AI Engineers, ML Engineers, and Applied AI candidates effectively
- Evaluating AI portfolios, demos, GitHub repositories, and research projects
- Creating AI interview questions and hiring scorecards
- Comparing AI Engineering, Machine Learning, Data Science, and LLM Engineering roles
- Understanding AI infrastructure and production AI workflows
- Identifying AI seniority levels and skill expectations
- Understanding generative AI, autonomous agents, and multimodal systems
- Writing AI-related job descriptions and hiring requirements
- Explaining AI terminology used by engineers and researchers
- Understanding collaboration between AI, data, backend, product, and infrastructure teams
What AI engineering means in 2026
Modern AI engineering is no longer:
- "just training machine learning models"
- "only building chatbots"
- "just prompt engineering"
In 2026, modern AI systems increasingly include:
- LLM applications
- agentic AI systems
- multimodal AI
- retrieval-augmented generation (RAG)
- AI infrastructure
- vector databases
- AI observability
- autonomous workflows
- AI orchestration
- AI product integration
Modern AI teams are increasingly expected to support:
- product automation
- intelligent workflows
- AI copilots
- enterprise AI systems
- recommendation systems
- AI-driven analytics
- AI-assisted software development
Agentic AI and multi-agent systems are becoming major industry trends in 2026.
AI ecosystem (2026)
Core AI and ML frameworks
- PyTorch
- TensorFlow
- Scikit-learn
- JAX
Generative AI and LLM ecosystems
- OpenAI APIs
- Anthropic APIs
- Hugging Face
- LangChain
- LlamaIndex
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 · 366 lines · 90 tokens per session scan A 4ae71e6795d2
hr-ai is a skill published in the GitHub repository tuanductran/hr-skills (57 stars, last pushed 2d ago), licensed MIT. It adds 90 tokens to every session and 2,177 once invoked, about $0.0005 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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