schema-gap-analysis

What problem does it solve?

This Skill detects schema and data drift in CultureMech by running LinkML validation and classifying resulting failures into schema, instance, and process-related gaps.

Core Features & Use Cases

• Fast LinkML validation with error counting: runs linkml-validate over the canonical merged dataset (and optionally the raw normalized layer) and summarizes how many errors exist so you can quickly judge whether a change broke validation.
• Error histograms for triage: groups common failure modes (additional properties, missing required fields, pattern/type mismatches, and wrong value formats) to help you prioritize the most frequent issues.
• Axis-3 drift heuristics (process checks): performs targeted greps against Python sources to catch common writer/generator anti-patterns like renamed fields, timestamp/date mismatches, and metadata dropping in YAML serialization.
• Recommended fix roadmap: surfaces CultureMech-specific known gap classes (e.g., curation_history date/timestamp, preparation_steps instruction/description, references reference_id/reference) to speed up remediation.

Quick Start

Run schema-gap-analysis to validate merged_2026 YAML files and get an error-focused histogram of the most likely drift sources.