# Sourcing Strategy

> Build multi-channel sourcing strategies for hard-to-fill roles with Boolean search, outreach sequences, and pipeline tracking

- Skill: `uitbreidenos/sourcing-strategy` (Agent Skill)
- Install (CLI): `npx skillmds@latest add uitbreidenos/sourcing-strategy`
- Raw SKILL.md: https://api.skillmd.com/api/skills/uitbreidenos/sourcing-strategy/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: UitbreidenOS (https://skillmd.com/u/uitbreidenos)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/uitbreidenos/sourcing-strategy

---


## When to activate

- Sourcing passive candidates for niche or senior roles
- Building Boolean search strings for LinkedIn, GitHub, StackOverflow
- Designing multi-touch outreach sequences
- Evaluating sourcing channel effectiveness
- Creating talent maps for recurring hiring needs

## When NOT to use

- For posting job ads on job boards (inbound)
- For agency/vendor management
- For recruitment marketing campaigns

## Instructions

1. **Define ideal candidate profile.** Skills, experience, company targets, location, and deal-breakers.
2. **Build Boolean searches.** Create 3-5 search strings per platform (LinkedIn Recruiter, GitHub, StackOverflow, X-Ray).
3. **Identify target companies.** List 10-20 companies with similar tech stack or industry for poaching.
4. **Design outreach sequence.** 4-touch sequence over 14 days: InMail → follow-up → value-add content → final nudge.
5. **Personalize outreach.** Reference candidate's specific project, talk, blog post, or open-source contribution.
6. **Track pipeline.** Source → responded → interested → screening → interview → offer. Target 10% response rate minimum.
7. **Report channel ROI.** Cost per hire, response rate, and quality score by sourcing channel.

## Example

```
Role: Staff ML Engineer
Boolean (LinkedIn): ("machine learning" OR "deep learning") AND ("PyTorch" OR "TensorFlow") AND ("MLOps" OR "model serving") AND ("senior" OR "staff" OR "lead")
Target Companies: Scale AI, Anyscale, Hugging Face, Weights & Biases, Databricks

Outreach Sequence:
Day 0: InMail — reference their recent paper/talk
Day 4: Follow-up — share relevant blog post on your tech stack
Day 9: "Quick question" — ask about their experience with [specific tool]
Day 14: Final — "Keeping this open until Friday"
```