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schema-gap-analysis

Validate CultureMech LinkML YAML instances and cluster validation failures across schema, instance, and process axes.

10|3|Updated Jan 28, 2026
Search Tags:#drift detection#data quality#data pipeline#YAML#schema validation#linkml#taxonomy grounding
One-click install
npx skills add https://github.com/CultureBotAI/CultureMech --skill schema-gap-analysis
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: schema-gap-analysis
Source: https://github.com/CultureBotAI/CultureMech/tree/main/.claude/skills/schema-gap-analysis
Command: npx skills add https://github.com/CultureBotAI/CultureMech --skill schema-gap-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

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.

Frequently Asked Questions about schema-gap-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I detect schema drift in YAML data pipelines before committing changes?▼

To detect schema drift in YAML data pipelines, you run LinkML validation against the canonical merged dataset to count structural deviations. This groups validation failures into actionable error histograms for rapid triage.

What is LinkML schema validation used for in data quality checks?▼

LinkML schema validation is used in data quality checks to verify that YAML instances conform to a defined schema contract. It identifies missing required fields, additional properties, pattern mismatches, and wrong value formats to maintain dataset integrity.

How to validate YAML instances against a LinkML schema contract?▼

You validate YAML instances against a LinkML schema contract by running linkml-validate over the canonical merged dataset. The process summarizes total errors and clusters common failure modes like missing required fields and type mismatches for prioritized remediation.

Can I run schema validation offline without external API dependencies?▼

Yes, you can run schema validation offline because the process relies entirely on local linkml-validate execution and targeted greps against Python sources. It requires no external API calls to produce error histograms and drift heuristics.

Why does my data pipeline keep dropping metadata fields during YAML serialization?▼

Your data pipeline drops metadata fields during YAML serialization due to common writer and generator anti-patterns. Targeted greps against Python sources can catch these process-related drift issues, including renamed fields and timestamp mismatches.

What is the best way to group LinkML validation errors for triage?▼

The best way to group LinkML validation errors for triage is to cluster them into error histograms by failure mode. This categorizes issues across schema, instance, and process axes to help you prioritize the most frequent drift sources.

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