data-sensibility

What problem does it solve?

Build intuition about document data before extraction design. Use before designing any extraction schema or regex pattern, when onboarding a new document type, or when extraction accuracy is unexpectedly low and you suspect a data assumption is wrong. It covers systematic observation of raw documents, spot-checking extracted results, distribution analysis, and recognizing suspicious patterns. If you are about to write code that touches document data and you have not read at least five documents end-to-end, stop and use this skill first.

Core Features & Use Cases

• Systematic data observation: read 3-5 representative documents to surface stable anchors and variability.
• Data quality checks: spot-check values, detect pattern mismatches, and identify anomalous fields before coding.
• Use Case: onboarding a new document type and validating data assumptions to reduce extraction errors.

Quick Start

Read 3-5 complete documents of a new type to surface data patterns before designing any extraction logic.