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