Repeatability
High
The transformation logic is identical for every file: parse JSON, extract defined fields, flatten nested product data, and join on SKU. No per-file judgment is needed, making this highly automatable.
Ambiguity Tolerance
High
Output columns are explicitly named, the category-fill rule is deterministic (SKU lookup against a reference sheet), and success is measurable by row count and null-check on required fields.
Data & Tool Availability
High
The user is providing both the JSON files and the reference spreadsheet, so all inputs are available. A code agent with file-system access and a Python/pandas environment has everything it needs.
Error Cost
Low
The output is a new CSV file — the source JSON files are untouched. Any errors are easily spotted by spot-checking rows and are fully reversible by re-running the script.
Human Judgment Required
Low
Category assignment is rule-based via SKU matching, not subjective. The only edge case requiring human input would be SKUs absent from the reference sheet, which the agent can flag rather than guess.