What is the primary purpose of the Matching Baseline skill?

The skill implements a rule-based system to match CVs with Job Descriptions, ensuring that scoring is both deterministic and easy to explain.

Does this skill use Machine Learning for matching?

No, this skill focuses on a rule-based baseline. While ML is planned for future phases, this skill ensures a stable and transparent foundation first.

Which files are typically modified when using this skill?

The skill primarily guides updates to src/core/matcher.py for logic and tests/unit/test_matcher.py for validation.

How do I modify the scoring weights?

You can adjust the scoring logic and weights by following the skill's guidance to update the src/core/matcher.py file.

How does the skill handle explainability?

It ensures that every match result includes a breakdown of criteria, clearly showing where a candidate matches or has gaps relative to the job description.

CV-JD Matching Baseline

Name: CV-JD Matching Baseline
Author: gw-ai-security

bygw-ai-security

Ciencia de Datos y ML

Implements rule-based resume and job description matching with deterministic scoring and detailed explainability.

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The CV-JD Matching Baseline skill provides a standardized framework for comparing resumes against job descriptions using a robust, rule-based approach. It prioritizes deterministic results and high explainability, enabling developers to implement transparent scoring logic, adjust criteria weighting, and generate clear 'match vs. gap' breakdowns. This skill is essential for building trustworthy recruitment tools where understanding the 'why' behind a score is as important as the score itself.

Características Principales

Clear architectural compliance with ADR-002
Deterministic rule-based scoring logic
Adjustable criteria weighting for scoring optimization
0 GitHub stars
Automated unit test alignment for matcher logic
Detailed explainability with match/gap analysis

Casos de Uso

Developing a transparent Applicant Tracking System (ATS)
Generating candidate feedback reports based on job criteria
Refining recruitment scoring logic to reduce bias

Acerca de

Características Principales

Clear architectural compliance with ADR-002
Deterministic rule-based scoring logic
Adjustable criteria weighting for scoring optimization
0 GitHub stars
Automated unit test alignment for matcher logic
Detailed explainability with match/gap analysis

Casos de Uso

Developing a transparent Applicant Tracking System (ATS)
Generating candidate feedback reports based on job criteria
Refining recruitment scoring logic to reduce bias