webshop-query-parser

Name: webshop-query-parser
Availability: InStock
Author: ZJUNLP

Parse natural language shopping queries into structured product attributes and price constraints.

1.1k|131|Updated Jan 10, 2026

Search Tags:#data extraction #e-commerce #natural language processing #product search #query parsing #shopping assistant

Ready to run

npx skills add https://github.com/zjunlp/SkillNet --skill webshop-query-parser

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: webshop-query-parser
Source: https://github.com/zjunlp/SkillNet/tree/main/experiments/src/skills/webshop/webshop-query-parser
Command: npx skills add https://github.com/zjunlp/SkillNet --skill webshop-query-parser

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This skill automates the extraction of crucial details from user shopping requests, transforming unstructured natural language into actionable search criteria.

Core Features & Use Cases

Structured Data Extraction: Identifies product names, attributes, price limits, and other specifications from user queries.
Search Strategy Formulation: Generates precise keywords for web searches based on extracted criteria.
Use Case: When a user says, "I need a vegan, gluten-free pizza oven under $300," this skill will parse it into structured data, enabling the agent to perform an effective search like search[vegan gluten-free pizza oven] and filter results by price.

Quick Start

Use the webshop-query-parser skill to extract search criteria from the user's instruction "find me organic apples, not too expensive".

Frequently Asked Questions about webshop-query-parser

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

FAQPage Schema

How do I parse natural language shopping queries for e-commerce search?▼

To parse natural language shopping queries, you extract product types, attributes, and price constraints from unstructured text. This skill analyzes user requests to identify key entities, generating structured data and precise keywords for e-commerce platform searches.

What is the best way to extract product attributes and price limits from user search text?▼

The best way to extract product attributes and price limits is to use a query parser that identifies specific entities within natural language. This skill structures unstructured requests into actionable search criteria like product names and maximum price filters.

Can I use Python scripts to structure unstructured e-commerce search requests?▼

Yes, you can use Python scripts to structure unstructured e-commerce search requests. This skill requires a Python script to analyze query components, identify key product specifications, and formulate actionable search strategies from user instructions.

Does query parsing for shopping assistants support complex multi-attribute requests?▼

Query parsing for shopping assistants supports complex multi-attribute requests by extracting multiple specifications simultaneously. It handles unstructured natural language containing product types, dietary attributes, and price constraints to generate precise search formulations.

How do I generate precise search keywords from unstructured customer instructions?▼

You generate precise search keywords from unstructured customer instructions by applying natural language processing to identify key entities. This skill formulates search strategies by extracting product criteria and structuring them into actionable query formats.

When should I not use automated query parsing for e-commerce product searches?▼

Automated query parsing for e-commerce product searches may not be suitable when basic implementation depth is insufficient for highly ambiguous natural language. It handles structured data extraction but may struggle with deeply contextual or unstructured conversational nuances.