iris-interface-field-match

To normalize IRIS interface fields from a parsed JSON file, execute the provided Python script via command line. The process standardizes field codes, names, types, lengths, and mandatory flags to ensure data consistency before manual review or downstream integration.

Semantic candidate matching for interface data compares parsed fields to provide semantic matches, confidence scores, and evidence. It standardizes varying field definitions and identifies corresponding data points to improve data review efficiency in interface management workflows.

Yes, you can diagnose unmatched fields and missing mandatory flags in IRIS data. The script lists specific reasons for unmatched fields, such as missing codes, ambiguous names, type conflicts, and missing local feedback, facilitating targeted manual review.

Yes, you need Python to run semantic normalization and matching on IRIS interfaces. The Skill requires Python script execution for processing. You must run it in your project root directory, specifying the parsed JSON file path and project root.

You generate feedback drafts for unmatched IRIS fields by executing the field matching Python script. It automatically creates local feedback drafts for manual review and confirmation, addressing issues like ambiguous names or type conflicts found during the matching process.

The best way to post-process parsed JSON outputs for IRIS interface data is using a dedicated semantic normalization script. It standardizes fields, matches candidates with confidence scores, and diagnoses unmatched entries to ensure data quality before further analysis.