ai-non-deep-user-inspection

This Skill filters non-deep users from an AI inspection Excel report by parsing rows where the field 是否深度用户 equals 否. It then exports the extracted user details into a JSON array for daily reporting.

Yes, you can use pandas and Playwright to automate daily AI reporting data extraction. Python with pandas reads the Excel report to filter user depth data, while Playwright handles downloading the report for JSON export.

The best way to export a JSON array of specific user details from Excel is using Python with pandas to read and filter the spreadsheet rows. This approach extracts matching records and writes them directly into a JSON array format.

Yes, the daily AI inspection workflow requires a Python environment with pandas and Playwright installed. These dependencies are necessary to read the Excel report, filter the non-deep user rows, and write the final JSON output.

You integrate non-deep user JSON data with JoyClaw by running the inspection script to generate a daily JSON file. This file contains the filtered array of user details, which JoyClaw consumes for targeted outreach and reporting.

A limitation of using Excel reports for identifying AI non-deep users is the strict dependency on the exact column name 是否深度用户. If the report schema changes or the field is missing, the pandas filtering logic will fail to export the JSON.