What is the role of MCP in this skill?

The Playwright MCP (Model Context Protocol) is used for real-time validation and reporting, ensuring every step of the automation is verified during execution.

What is the primary benefit of using this skill?

It automates the tedious process of replacing hardcoded values in Playwright scripts with dynamic lookups from Excel, making your automation more scalable.

How does it handle data loading?

It uses the Polars library to load Excel (.xlsx) data efficiently, ensuring high performance even with larger spreadsheets.

Can I use this for existing Playwright scripts?

Absolutely. It is specifically built to analyze existing codegen scripts and refactor them to use centralized configurations and external data.

Does it require a specific Python environment?

Yes, it is designed to work within a Conda environment named 'playwright' to ensure dependency consistency and avoid virtual environment conflicts.

Playwright Excel Automator

Name: Playwright Excel Automator
Author: Rukkha1024

byRukkha1024

Browser Automation

Transforms static Playwright scripts into dynamic, data-driven automations using Excel and centralized configurations.

About

This skill streamlines the transition from hardcoded Playwright codegen scripts to robust, Excel-backed automation workflows. By leveraging Polars for high-performance data loading and implementing centralized YAML configurations, it enables developers to manage test data and environmental variables outside of the codebase. The skill integrates seamlessly with the Playwright MCP for real-time validation and reporting, ensuring that automated browser tasks remain accurate and maintainable even as data sets grow.

Key Features

0 GitHub stars
Integrated Playwright MCP validation for real-time reporting and error checking
Automatic conversion of hardcoded Playwright values into Excel-driven lookups
MD5 checksum verification for ensuring output consistency during logic refactoring
Centralized configuration management via YAML for paths, patterns, and constants
High-performance data loading utilizing the Polars library

Use Cases

Centralizing browser automation configurations for multi-environment deployments
Automating repetitive web forms using bulk data stored in Excel spreadsheets
Converting basic Playwright recordings into scalable data-driven test suites

About

Key Features

0 GitHub stars
Integrated Playwright MCP validation for real-time reporting and error checking
Automatic conversion of hardcoded Playwright values into Excel-driven lookups
MD5 checksum verification for ensuring output consistency during logic refactoring
Centralized configuration management via YAML for paths, patterns, and constants
High-performance data loading utilizing the Polars library

Use Cases

Centralizing browser automation configurations for multi-environment deployments
Automating repetitive web forms using bulk data stored in Excel spreadsheets
Converting basic Playwright recordings into scalable data-driven test suites