CSV Import Contract Repair

The problem

CSV and spreadsheet imports often suffer from contract drift where preview logic, execution workers, and retry handlers interpret files differently. This leads to silent data corruption, duplicate records, and tenant-scope leaks when encoding, locales, or mapping rules vary across the stack.

What it does

• Identifies drift between preview parsers and background execution workers.
• Detects encoding, delimiter, and byte-order mark (BOM) inconsistencies.
• Adds version-binding to ensure execution matches the exact file digest and mapping confirmed by the user.
• Implements idempotent row application to prevent duplicate writes during job retries.
• Neutralizes dangerous formulas and external links in generated error export files.

Frameworks & tools

Works with repository-based import logic including pandas, openpyxl, Prisma, SQLAlchemy, BullMQ, Celery, and standard CSV parsers.

Why this beats prompting it yourself

Generic prompts often miss the subtle lifecycle gaps between a web-based preview and a background worker. This skill enforces a strict provenance chain from raw bytes to persisted row outcomes, ensuring local verification remains deterministic without needing production data access.

Use cases

• Fixing column-shift risks where headers are mapped differently in preview versus execution.
• Standardizing locale-sensitive date and decimal parsing across heterogeneous environments.
• Securing multi-tenant imports where referenced IDs must be resolved within strict ownership boundaries.
• Hardening batch rollback logic to target only mutations owned by a specific import.

Known limitations

Does not access production databases, execute live migrations, or certify the accuracy of historical data already imported into the system.