Why use rule-based parsing instead of machine learning?

Rule-based logic provides better explainability, fewer dependencies, and easier debugging for ATS systems where transparency in matching is critical.

How do I add a new field to the parser?

You can use this skill to guide the update of the core parsing logic in jd_parser.py and automatically adapt the corresponding unit tests.

Does it support multiple languages?

Yes, it currently provides optimized parsing logic and headers for both German and English job descriptions.

What is the jd-parsing skill used for?

It is designed to extract structured fields and requirements from job descriptions for use in recruitment software and candidate matching.

Job Description Parser

Name: Job Description Parser
Author: gw-ai-security

bygw-ai-security

ウェブスクレイピングとデータ収集

Extracts structured requirements and metadata from job descriptions to facilitate automated candidate matching and recruitment analysis.

概要

The jd-parsing skill enables Claude to efficiently process and structure job descriptions (JDs) in both German and English. By leveraging a rule-based parsing engine, it identifies key fields, headers, and requirements while maintaining high explainability and testability without the overhead of heavy machine learning models. This skill is ideal for developers working on Applicant Tracking Systems (ATS) or CV scoring tools who need to convert unstructured text into clean data for downstream matching algorithms and HR workflows.

主な機能

Multilingual support for German and English job headers
Structured data output for integration with matching engines
Automated test generation and updates for new JD fields
0 GitHub stars
Rule-based extraction for high explainability and reliability
Graceful handling and error reporting for missing input fields

ユースケース

Standardizing diverse job postings into a structured database format
Building or enhancing an automated CV scoring and candidate matching system
Expanding recruitment software to support new languages or custom data fields

概要

主な機能

Multilingual support for German and English job headers
Structured data output for integration with matching engines
Automated test generation and updates for new JD fields
0 GitHub stars
Rule-based extraction for high explainability and reliability
Graceful handling and error reporting for missing input fields

ユースケース

Standardizing diverse job postings into a structured database format
Building or enhancing an automated CV scoring and candidate matching system
Expanding recruitment software to support new languages or custom data fields