---
title: Agent use case: turn 200 PDFs and 2,386 emails into one transaction spreadsheet
description: OpenClaw pulled 119 categorized transactions out of 200 exported PDFs, then wrote a Python script on the fly to parse 2,386 email files into one Excel spreadsheet.
---
title: Agent use case: turn 200 PDFs and 2,386 emails into one transaction spreadsheet
description: OpenClaw pulled 119 categorized transactions out of 200 exported PDFs, then wrote a Python script on the fly to parse 2,386 email files into one Excel spreadsheet.
---
Agent use case: turn 200 PDFs and 2,386 emails into one transaction spreadsheet
The point
A user pointed OpenClaw at 200 exported PDFs and asked it to extract all transactions with categories. It pulled out 119 transactions, then wrote a Python script on the fly to parse 2,386 email files and put all the results into an Excel file.
Why it matters
Evidence: user-reported, from the Good Bot case list at goodbot.guide, not independently verified.
Key details
- Categorization was the weak spot: the user said vague transaction names made accurate categories effectively impossible for the model
- Model used for this run: Sonnet 4.5
Original ~12 min → 25 sec here Source: Reddit · openclaw via Good Bot, Apr 12, 2026