Taxi Expense

识别滴滴打车订单截图，OCR识别文字+坐标，马赛克脱敏目的地，按月生成报销Excel

This skill appears to do what it says, but check a few practical points before use: - Paths: process.js reads/writes taxi_data.json and screenshots relative to the scripts directory, while SKILL.md/README reference ~/.openclaw/workspace/taxi_expense/ — decide which path you want and adjust files or move/copy taxi_data.json and tessdata accordingly. - Create initial files/dirs: create an initial taxi_data.json (an empty JSON array: []) and the screenshots directory in the same directory as process.js to avoid crashes. - Tesseract traineddata: setup.sh writes tessdata into the skill's tessdata directory, but process.js config points to /tmp/tessdata. Either place traineddata in /tmp/tessdata or edit process.js to point to the skill tessdata directory. - Network & build: setup.sh runs npm install and downloads large traineddata files from GitHub; ensure the host environment allows network access and has the build/runtime requirements for sharp (native binaries). Use a proxy if required (http_proxy/HTTP_PROXY supported). - Review generated output before sharing: the script masks destination text and images, but OCR errors and masking heuristics can produce edge cases — verify sensitive content in produced XLSX and screenshots before sending to others. - Audit before automation: the code performs only local I/O and image processing. If you plan to add automated sending (openclaw message send), confirm recipient/channel configuration to avoid accidental data exfiltration. If you want, I can suggest minimal edits to process.js and setup.sh to make paths consistent, add safe existence checks (create taxi_data.json if missing), and make the tessdata path configurable.

Type: OpenClaw Skill Name: taxi-expense Version: 1.1.0 The taxi-expense skill is a legitimate tool for automating expense reports from Didi taxi screenshots. It uses Tesseract.js for OCR, Sharp for image processing (including privacy-focused masking of destination addresses), and ExcelJS for report generation. The code logic in scripts/process.js and scripts/setup.sh is transparent, follows the stated purpose, and includes helpful error-correction for OCR inaccuracies without any signs of data exfiltration or malicious intent.