playwright-excel

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Integrate Excel (.xlsx) data into Playwright codegen scripts by replacing hardcoded values with config-driven lookups, loading data with polars, and validating every step with Playwright MCP (start MCP from the repo if not running). Use for Playwright automation updates that require Excel-backed data, config.yaml centralization, or MCP validation/reporting.

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[Web Automation]

When & Why to Use This Skill

This Claude skill enables the transformation of static Playwright scripts into robust, data-driven web automation workflows by integrating Excel (.xlsx) data. It automates the replacement of hardcoded values with dynamic lookups, leverages Polars for efficient data handling, and incorporates Playwright MCP validation to ensure high reliability and centralized configuration management.

Use Cases

  • Data-Driven Testing: Automatically execute Playwright test cases using multiple data sets sourced from Excel spreadsheets to increase test coverage and efficiency.
  • Automated Bulk Data Entry: Streamline web-based data entry tasks by mapping Excel columns to Playwright actions for efficient, error-free form processing.
  • Script Maintenance and Refactoring: Convert hardcoded automation scripts into maintainable, config-driven pipelines with centralized control via config.yaml and standardized data loading.
  • Automated Validation and Reporting: Use Playwright MCP and MD5 checksums to validate automation outputs and ensure consistency across complex web-based workflows.
nameplaywright-excel
descriptionIntegrate Excel (.xlsx) data into Playwright codegen scripts by replacing hardcoded values with config-driven lookups, loading data with polars, and validating every step with Playwright MCP (start MCP from the repo if not running). Use for Playwright automation updates that require Excel-backed data, config.yaml centralization, or MCP validation/reporting.

Playwright Excel Integration

Overview

Convert Playwright codegen scripts into Excel-driven automations with centralized config and required MCP validation.

Environment

  • Use the playwright conda environment.
  • Before running any Python command, run: conda run -n playwright python -c "import sys; print(sys.executable)"
  • Do not create or activate any venv or .venv.

Inputs

  • Playwright codegen script path
  • Excel .xlsx path
  • Mapping lines: "hardcoded_value" -> Excel[Sheet][FilterCol==FilterVal][DataCol]
  • Optional override: PLAYWRIGHT_TARGET_SUBJECT

Workflow

  1. Analyze the Playwright script and the Excel structure (sheets, columns, sample rows).
  2. Detect hardcoded .fill() values and confirm that each has a mapping; request clarification for mismatches.
  3. Ensure dependencies in the playwright conda env (prefer conda install -n playwright, fall back to conda run -n playwright pip install).
  4. Create or update config.yaml using centralized control (paths, patterns, column definitions, constants, tunables, shared texts).
  5. Modify the Playwright script:
    • Add a config loader and an Excel loader (polars; see references/excel-loading.md).
    • Replace hardcoded values with data[...].
  6. Always run Playwright MCP validation; if MCP is not running, start it from this repo before continuing (see references/mcp-validation.md).
  7. When refactoring existing pipelines/logic, generate outputs and compare MD5 checksums with reference files (see references/md5-validation.md).
  8. Run the updated script with conda run -n playwright python.

References

  • references/excel-loading.md
  • references/mcp-validation.md
  • references/md5-validation.md
  • references/examples.md