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

• Analyze the Playwright script and the Excel structure (sheets, columns, sample rows).

• Detect hardcoded .fill() values and confirm that each has a mapping; request clarification for mismatches.

• Ensure dependencies in the playwright conda env (prefer conda install -n playwright , fall back to conda run -n playwright pip install ).

• Create or update config.yaml using centralized control (paths, patterns, column definitions, constants, tunables, shared texts).

• Modify the Playwright script:

• Add a config loader and an Excel loader (polars; see references/excel-loading.md ).

• Replace hardcoded values with data[...] .

• Always run Playwright MCP validation; if MCP is not running, start it from this repo before continuing (see references/mcp-validation.md ).

• When refactoring existing pipelines/logic, generate outputs and compare MD5 checksums with reference files (see references/md5-validation.md ).

• 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