candidate-personalization-at-scale

Generate personalized outreach subject lines and opening paragraphs from verified candidate signals.

Updated May 2, 2026
One-click install
npx skills add https://github.com/marius-bughiu/ooligo --skill candidate-personalization-at-scale
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: candidate-personalization-at-scale
Source: https://github.com/marius-bughiu/ooligo/tree/main/apps/web/public/artifacts/candidate-personalization-at-scale-skill
Command: npx skills add https://github.com/marius-bughiu/ooligo --skill candidate-personalization-at-scale

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill eliminates the low response rates and candidate disengagement caused by generic, mail-merge style recruiter outreach by enabling personalized, signal-grounded first touchpoints for sourced candidates, cutting down hours of manual profile research per open role.

Core Features & Use Cases

  • Signal-grounded personalization: Generates unique subject lines and opening paragraphs for each candidate using only verified public signals (GitHub activity, LinkedIn experience, recruiter notes) to avoid fabricated or irrelevant personalization details.
  • Built-in compliance guardrails: Automatically blocks personalization hooks that correlate with protected classes to reduce disparate treatment risk, with configurable thresholds for legal and HR team review.
  • Use Case: A recruiting team sourcing 50 senior infrastructure engineers can input a CSV of sourced candidates and the role's job description, and receive reviewed-ready personalized outreach drafts for each candidate that reference their actual public work, ready for bulk sequence enrollment.

Quick Start

Provide the skill with a sourced candidate's name, title, current company, LinkedIn URL, optional GitHub handle, and the target role's job description to receive a personalized subject line and opening paragraph for your outreach message.

Frequently Asked Questions about candidate-personalization-at-scale

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

FAQPage Schema
How do I personalize candidate outreach emails using GitHub and LinkedIn signals?

Personalize candidate outreach by generating unique subject lines and opening paragraphs from verified public GitHub activity, LinkedIn experience, and recruiter notes, ensuring all claims are verifiable to avoid fabricated details.

What's the best way to scale personalized recruiter emails without risking hiring compliance violations?

Scale personalized recruiter emails safely by using built-in compliance guardrails that block personalization hooks correlating with protected classes, reducing disparate treatment risk with configurable thresholds for legal review.

How do I bulk generate personalized first-touch messages for sourced candidates?

Bulk generate first-touch messages by inputting a CSV of sourced candidates and the target job description, then receiving reviewed-ready personalized outreach drafts referencing actual public work for each candidate.

Can I use GitHub handles and LinkedIn URLs to improve candidate response rates?

Use GitHub handles and LinkedIn URLs to ground outreach in verified public signals, replacing generic mail-merge templates with relevant personalization that increases candidate engagement and response rates.

How do I prevent fabricated personalization details in automated candidate outreach?

Prevent fabricated personalization details by enforcing strict fabrication guards that ensure every generated claim in the outreach subject line and opening paragraph is directly verifiable from public input data.

What inputs do I need to generate personalized outreach for technical and non-technical roles?

Generate personalized outreach by providing the candidate's name, title, current company, LinkedIn URL, optional GitHub handle, and the target role's job description to receive a tailored subject line and opening paragraph.