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runtime-transform-checks

Validate runtime variable transformations in analysis scripts against workspace semantic rules.

Updated Apr 18, 2026
Search Tags:#analysis#validate#reproducibility#variables#data-quality#dmft#occl_tip
One-click install
npx skills add https://github.com/Centaurioun/osteogenesis_imperfecta --skill runtime-transform-checks
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: runtime-transform-checks
Source: https://github.com/Centaurioun/osteogenesis_imperfecta/tree/main/.claude/skills/runtime-transform-checks
Command: npx skills add https://github.com/Centaurioun/osteogenesis_imperfecta --skill runtime-transform-checks

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Prevents analysis errors and misinterpretation by validating that derived variables and runtime transformations in analysis code conform to workspace semantic rules and agreed statistical treatments.

Core Features & Use Cases

  • Rule-based variable checks: Verifies occl_tip handling (1–3 as Angle, 4 as infraocclusion), dmft_dmft usage as a count and its binary caries_any conversion, and runtime gene grouping thresholds.
  • Script inspection for compliance: Scans specified analysis scripts to detect ordinal vs count misuse and missing runtime grouping logic, producing actionable findings.
  • Use Case: Run before executing the active analysis to catch transformation mismatches that could explain numeric discrepancies or flawed statistical tests.

Quick Start

Validate occl_tip, dmft_dmft, and gene grouping in the active analysis script and return a structured JSON compliance report.

Frequently Asked Questions about runtime-transform-checks

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

FAQPage Schema
How do I validate variable transformations before running my analysis script?▼

To validate variable transformations before running an analysis script, you can scan the code to verify derived variables conform to predefined semantic rules, outputting a structured JSON compliance report detailing any mismatches.

Why does my data analysis show numeric discrepancies after runtime variable transformations?▼

Numeric discrepancies often occur when runtime variable transformations violate semantic rules, such as misusing ordinal values as counts or missing gene grouping thresholds, detectable by inspecting scripts before execution.

What is the correct way to handle occl_tip and dmft variables in dental data analysis?▼

Handling occl_tip and dmft variables correctly requires treating occl_tip values 1–3 as Angle classifications and 4 as infraocclusion, while using dmft_dmft strictly as a count for binary caries_any conversion.

Can I check my analysis code for missing gene grouping thresholds before execution?▼

Yes, you can check analysis code for missing runtime gene grouping thresholds by inspecting scripts for compliance against workspace semantic rules, which outputs actionable findings in a structured JSON format.

Does this validation approach detect ordinal vs count misuse in derived variables?▼

Yes, this validation approach detects ordinal vs count misuse by scanning specified analysis scripts to verify derived variables like dmft_dmft are used correctly as counts rather than ordinal measures.

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