match

Score recent job listings against a predefined seeker profile.

Updated Aug 27, 2026

Search Tags:#algorithm #scoring #profile #matching #recruiting #jobs

Ready to run

npx skills add https://github.com/EtanHey/golems --skill match-etanhey

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: match
Source: https://github.com/EtanHey/golems/tree/main/packages/jobs/skills/match
Command: npx skills add https://github.com/EtanHey/golems --skill match-etanhey

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the process of evaluating new job listings against a user's professional profile, identifying the most relevant opportunities.

Core Features & Use Cases

Automated Scoring: Ranks job postings based on skill, role, location, and salary compatibility.
Profile Matching: Leverages a predefined seeker profile for consistent evaluation.
Use Case: After a new batch of job postings is scraped, this Skill can automatically score them and flag any that are a strong match (score 8+) for the user to review.

Quick Start

Run the job matching algorithm on recent scrapes to score new listings against your profile.

Frequently Asked Questions about match

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

FAQPage Schema

How do I score job listings against my professional profile?▼

To score job listings against your professional profile, you can use an automated matching algorithm that evaluates skill, role level, location, and salary compatibility. It analyzes recent postings and stores the results in a database to identify relevant opportunities.

How does LLM evaluation work for automated job matching?▼

LLM evaluation for job matching analyzes your predefined seeker profile against recent job postings. It scores compatibility across skills, role level, location, and salary, storing these scores in a database and reporting on the overall match distribution.

Can I use predefined profile matching for automated recruiting scoring?▼

Yes, you can use a predefined seeker profile to perform automated recruiting scoring. The matching algorithm evaluates new job listings consistently against your profile, ranking postings and flagging strong matches with a score of 8 or higher.

What is the best way to filter scraped job postings for relevant opportunities?▼

The best way to filter scraped job postings is to run an automated scoring algorithm against a predefined seeker profile. This evaluates skill, location, and salary compatibility, triggering notifications for high-scoring positions to identify the most relevant opportunities.

What is needed to set up automated job profile scoring?▼

To set up automated job profile scoring, you need a predefined seeker profile and recent scraped job listings. The algorithm uses these inputs to evaluate compatibility, storing the resulting match scores in a database and reporting on the distribution.

Are there limitations to using an LLM for job matching and scoring?▼

A limitation of using LLM evaluation for job matching is that it relies strictly on a predefined seeker profile and recent scrapes. Compatibility is analyzed across skills, location, and salary, but scoring accuracy depends on the quality of the input profile and job data.