Stream CSV parsing and generation with Zod and Pydantic validation.
npx skills add https://github.com/CleanExpo/DR-NRPG --skill csv-processor-cleanexpo
Please help me install this Agent Skill. Skill: csv-processor Source: https://github.com/CleanExpo/DR-NRPG/tree/main/.skills/custom/csv-processor Command: npx skills add https://github.com/CleanExpo/DR-NRPG --skill csv-processor-cleanexpo
This Skill tackles the challenges of handling large CSV files efficiently and accurately, ensuring data integrity and correct formatting for both frontend and backend applications.
Use the csv-processor skill to import contractor data from the file 'contractors.csv'.
High-intent search queries and answers about installing and using this skill.
Streaming CSV parsing processes files row-by-row to prevent memory exhaustion when handling large datasets. This approach enables memory-safe chunked processing for both import and export scenarios across Next.js and FastAPI applications.
You can validate CSV data row by row in Next.js and FastAPI using Zod and Pydantic schemas respectively. This enforces data integrity during streaming CSV import by rejecting invalid rows before they enter your system.
Yes, this CSV streaming approach supports Australian locale formatting for dates and currency. It automatically handles DD/MM/YYYY date formats and AUD currency formatting during both CSV import parsing and export generation.
The best way to import a large CSV of contractor details is using row-by-row streaming with schema validation, then exporting a filtered list with locale-formatted dates. This ensures memory safety and data integrity across the full workflow.
Yes, you can use Zod for frontend CSV row validation and Pydantic for backend validation within a streaming pipeline. This dual validation ensures data integrity across Next.js and FastAPI environments during import.
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