One-click install
npx skills add https://github.com/tuanductran/hr-skills --skill hr-ai
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: hr-ai
Source: https://github.com/tuanductran/hr-skills/tree/main/skills/hr-ai
Command: npx skills add https://github.com/tuanductran/hr-skills --skill hr-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

hr-ai helps HR managers and recruiters understand AI/ML and make better hiring decisions by translating modern AI engineering concepts into clear, role-relevant expectations.

Core Features & Use Cases

  • AI/ML Role Understanding: Clarifies what AI engineers, ML engineers, applied AI engineers, research engineers, and AI infrastructure engineers do day to day.
  • Candidate Screening & Evaluation: Improves screening of portfolios, GitHub repos, demos, and research outputs using seniority-aware expectations and practical red-flag checks.
  • Modern AI Engineering Literacy: Explains key 2026 topics like LLM engineering, agentic systems, RAG, vector databases, observability, and AI infrastructure in recruiter-friendly terms.

Quick Start

Ask hr-ai to create a technical screening scorecard for a Senior AI Engineer role, including interview questions and rubric criteria.

Frequently Asked Questions about hr-ai

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I screen AI engineer candidates and evaluate their ML portfolios accurately?

Screen AI engineer candidates by applying seniority-aware expectations to their GitHub repos, demos, and research outputs, using practical red-flag checks to evaluate ML portfolios accurately.

What is the difference between an applied AI engineer and an AI infrastructure engineer?

The difference between an applied AI engineer and an AI infrastructure engineer lies in their daily focus: applied engineers build agentic systems and RAG, while infrastructure engineers manage vector databases and observability.

How do I create interview scorecards for LLM hiring and AI engineering roles?

Create interview scorecards for LLM hiring by generating role-specific interview questions and rubric criteria tailored to the expected seniority and technical requirements of AI engineering roles.

Can I use this to write job descriptions for AI, ML, and LLM-focused roles?

Yes, you can write job descriptions for AI, ML, and LLM-focused roles by translating modern AI engineering concepts into clear, role-relevant expectations for your hiring pipeline.

What AI terminology and concepts should recruiters understand for 2026 hiring?

Recruiters should understand AI terminology like LLM engineering, agentic systems, RAG, vector databases, and AI infrastructure to confidently assess candidates during 2026 hiring.

What are the limitations of using AI terminology guides for recruiting?

A limitation of using AI terminology guides is that they provide recruiter-friendly explanations and structured expectations, but do not replace hands-on technical verification of a candidate's actual coding or system design skills.