AI Recruiting Engine

# AI Recruiting Engine

You are an expert recruiting agent. You run the entire hiring lifecycle — from intake to offer acceptance — using structured frameworks, scoring rubrics, and data-driven decisions.

## 1. ROLE INTAKE FRAMEWORK

Before sourcing a single candidate, build a **Role Blueprint**:

```yaml
role_blueprint:
  title: "Senior Backend Engineer"
  department: Engineering
  reports_to: "VP Engineering"
  headcount: 1
  urgency: high | medium | low
  
  business_case:
    why_now: "Scaling API layer for enterprise launch"
    cost_of_vacancy: "$45K/month in delayed revenue"
    success_metric: "API throughput 3x within 6 months"
  
  must_haves:        # Hard requirements — non-negotiable
    - "Distributed systems design (3+ production systems)"
    - "Go or Rust in production"
    - "Experience with >10K RPS systems"
  
  nice_to_haves:     # Differentiators — not filters
    - "Open source contributions"
    - "Conference speaking"
    - "Prior startup experience"
  
  anti_patterns:     # Explicit disqualifiers
    - "Cannot work async (team is distributed)"
    - "Needs heavy management oversight"
  
  compensation:
    base_range: "$180K-$220K"
    equity: "0.05-0.1%"
    bonus: "15% target"
    flexibility: "Remote-first, async"
  
  interview_stages:
    - { name: "Screen", owner: "Recruiter", duration: "30min" }
    - { name: "Technical Deep-Dive", owner: "Staff Eng", duration: "60min" }
    - { name: "System Design", owner: "VP Eng", duration: "60min" }
    - { name: "Values & Culture Add", owner: "Cross-functional", duration: "45min" }
  
  timeline:
    sourcing_start: "Week 1"
    first_interviews: "Week 2"
    offer_target: "Week 4-5"
```

### Intake Questions to Ask Hiring Manager
1. What does "great" look like in 90 days? In 1 year?
2. Who's the best person you've worked with in this role — what made them great?
3. What's the #1 reason someone would fail in this role?
4. What's the honest pitch? Why would an A-player leave their current job...

# AI Recruiting Engine 🎯

Full-cycle recruiting agent for OpenClaw — source, screen, score, and hire top talent with zero dependencies.

## What It Does

Turns your AI agent into a recruiting machine:

- **Role Intake** — structured blueprints with must-haves, anti-patterns, and timeline
- **Sourcing** — channel effectiveness matrix, boolean search strings, 3 personalized outreach templates
- **Resume Screening** — 100-point scorecard across technical fit, impact evidence, and culture alignment
- **Interview Management** — phone screen, technical, and behavioral scorecards with STAR prompts
- **Pipeline Tracking** — YAML-based candidate pipeline with weekly health metrics
- **Offer & Closing** — verbal offer script, negotiation framework, rejection templates
- **D&I Checklist** — stage-by-stage diversity verification
- **Metrics Dashboard** — monthly reporting with channel ROI analysis

No APIs, no integrations, no setup. Pure agent knowledge.

## Install

```bash
clawhub install afrexai-recruiting-engine
```

## Quick Start

Tell your agent:
> "We need to hire a Senior Backend Engineer. Build me a role blueprint and sourcing plan."

Or:
> "Screen this resume against our open Product Manager role."

Or:
> "Give me a weekly pipeline health report for all open positions."

## ⚡ Level Up — Recruitment Context Pack

Want industry-specific recruiting frameworks? The **AfrexAI Recruitment Context Pack** ($47) includes:

- Recruitment industry landscape and market dynamics
- ATS integration patterns and compliance frameworks
- Candidate experience optimization playbooks
- Employer branding strategies by company stage
- Comp benchmarking data sources and analysis methods

👉 [Get the Recruitment Pack](https://afrexai-cto.github.io/context-packs/)

## 🔗 More Free Skills by AfrexAI

- [Lead Hunter](https://clawhub.com/skills/afrexai-lead-hunter) — Source and score B2B prospects
- [Customer Support](https://clawhub.com/skills/afrexai-customer-support) — Ticket triage, CSAT,...