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hr

Route HR requests into workflows and produce evidence-based artifacts.

415|44|Updated Mar 18, 2026
Search Tags:#compensation#recruiting#org planning#performance reviews#interview rubric#hr workflow#offer drafting
One-click install
npx skills add https://github.com/notque/vexjoy-agent --skill hr-notque
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: hr
Source: https://github.com/notque/vexjoy-agent/tree/main/skills/business/hr
Command: npx skills add https://github.com/notque/vexjoy-agent --skill hr-notque

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill prevents unreliable, biased, or legally risky HR output by forcing evidence-based decisions and clear guardrails across recruiting, performance management, compensation, offers, interviews, onboarding, and org planning.

Core Features & Use Cases

  • Mode-based HR execution: Automatically routes the request into the right workflow (recruiting, performance, compensation, offer drafting, interview design, onboarding, org planning, people analytics, or policy lookup).
  • Evidence-first gating: Produces hiring, review, compensation, and org artifacts that explicitly rely on supporting evidence (e.g., test outputs, documented examples, and cited data sources).
  • HR-specific safety guardrails: Reduces bias in language, avoids fabricated market numbers, requests jurisdiction for compliance, and flags binding documents for legal review.
  • Use Cases:
    • Build a structured recruiting plan with interview competencies and rubrics.
    • Draft a behavior-based performance review and development plan.
    • Benchmark compensation from user-provided/public data and generate defensible recommendations.
    • Create onboarding checklists and 30/60/90 goals.
    • Model headcount and design an org structure with success metrics and sequencing.

Quick Start

Tell the agent: "Plan my recruiting pipeline for a Senior Backend Engineer role, including stages, interview competencies, a scoring rubric, and a debrief template."

Frequently Asked Questions about hr

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

FAQPage Schema
How do I create an evidence-based performance review with bias-safe language?▼

To create an evidence-based performance review, the workflow requires documented examples and test outputs as supporting evidence, applying bias-safe language and explicit data-source limitations to produce structured behavior-based evaluations.

What is the best way to draft a compensation offer with defensible benchmarking?▼

Drafting a compensation offer uses user-provided or public market data for benchmarking, applying strict guardrails to avoid fabricated numbers and requesting jurisdiction clarification to generate defensible recommendations.

How do I build a recruiting pipeline with interview competencies and a scoring rubric?▼

Building a recruiting pipeline involves mode-based HR execution that structures end-to-end lifecycle stages, defines interview competencies, generates a scoring rubric, and produces a debrief template for calibration.

Can I use this for organizational planning and headcount modeling?▼

Yes, organizational planning is supported through people-analytics outputs that model headcount, design org structures, and define success metrics with sequencing.

Do I need legal review for onboarding checklists and binding HR documents?▼

Yes, the workflow enforces legal-review flags for binding HR documents, applies PII minimization, and requires jurisdiction clarification for compliance alongside generated onboarding checklists and 30/60/90 goals.

Why does the HR workflow require explicit data-source limitations?▼

Explicit data-source limitations are required to prevent unreliable or legally risky HR output, ensuring that compensation benchmarking and performance artifacts avoid fabricated market numbers and bias.

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