OpenClaw Bot Automates KMZ Data Extraction and Spreadsheet Merging

A developer shared a practical workflow using OpenClaw bot to automate data extraction from KMZ files and spreadsheet management.
Workflow Details
The user works with mapping data, specifically KMZ Google Earth files containing ID numbers, street names, and other data that needs to be transferred to spreadsheets for reporting. They directed OpenClaw bot to:
- Parse KMZ data from a specified drive location
- Extract exactly eight data points from the files
- Import the extracted data into a spreadsheet
- Calculate decimal mile markers between whole mile markers (e.g., 1.5 between markers 1 and 2) with high accuracy
- Merge new data with an existing spreadsheet, creating new lines without overwriting existing data
- Place new data in the correct columns automatically
Performance Metrics
The complete process required:
- Approximately 5 minutes of processing time
- About 15% of the "Current Session" usage on a $100 maximum plan
- Reported time savings: at least 10 hours compared to manual processing
The user noted they've been installing skills and extracting specific features from them, suggesting OpenClaw's functionality can be customized through skill installation and feature extraction.
📖 Read the full source: r/openclaw
👀 See Also
Three Minds: A Framework for Human + Two AI Agents Working Together
A Reddit user describes a human-AI collaboration pattern using two Claude agents with different contexts: one for daily operations, one for specialized domain expertise. The human provides direction and final decisions.

Connecting Claude to Canva via API for automated design generation
Reddit user describes connecting Claude to Canva via API, enabling plain-English prompts to generate editable Canva files with adjusted fonts, spacing, and layout, saving hours per week.

OpenClaw user struggles with AI agent automation after successful Claude Code pipeline
A marketing agency owner successfully created an image recreation pipeline using Claude Code in one hour, but encountered problems when trying to teach the same process to an AI agent in OpenClaw running on Gemini 3.1 Pro, with issues including bad reasoning, slow responses, and incorrect outputs.

Claude debugging case: Agent failed silently due to missing parameter, framing mattered more than model
A developer used Claude to build a calendar agent, then spent 40 minutes having Claude debug it before realizing the write_calendar tool lacked an attendees parameter. When given full context, Claude identified the issue in 10 seconds.