sdrf:validate

Validate SDRF files for structural, template, and ontology issues.

11|9|Updated Mar 18, 2026
One-click install
npx skills add https://github.com/bigbio/sdrf-skills --skill sdrf-validate
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: sdrf:validate
Source: https://github.com/bigbio/sdrf-skills/tree/main/skills/sdrf-validate
Command: npx skills add https://github.com/bigbio/sdrf-skills --skill sdrf-validate

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Researchers and annotators need a reliable way to ensure that SDRF files are structurally correct, follow the appropriate templates, and contain valid ontology terms. Manual checks are time‑consuming and error‑prone.

Core Features & Use Cases

  • Comprehensive structural checks: verifies column naming, required columns per template, and value formats.
  • Ontology validation via OLS: ensures organism, disease, tissue, instrument, and modification terms are accurate.
  • Consistency analysis: detects duplicate rows, mismatched replicates, and instrument inconsistencies across the entire file.

Quick Start

Ask the assistant to validate my SDRF file at 'experiment.sdrf' and report any errors.

Frequently Asked Questions about sdrf:validate

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

FAQPage Schema
How do I validate an SDRF file for proteomics experiments?

To validate an SDRF file, you can use automated tools to parse the document and check structural, template, and ontology issues. This process verifies required columns, value formats, and ontology term validity using OLS lookup.

What does ontology validation check for in SDRF datasets?

Ontology validation in SDRF datasets checks organism, disease, tissue, instrument, and modification terms against the OLS ontology lookup service. It ensures all annotated terms are accurate and compliant with the specification.

Why does my SDRF file have instrument inconsistencies and mismatched replicates?

SDRF files often contain instrument inconsistencies and mismatched replicates due to manual annotation errors. Consistency analysis detects duplicate rows and cross-file mismatches across the entire dataset automatically.

Can I check SDRF column names and required template columns automatically?

Yes, comprehensive structural checks can automatically verify SDRF column naming and required columns per template. This ensures your proteomics dataset follows the correct formatting rules instantly.

What is the best way to parse SDRF files and catch annotation errors instantly?

The best way to parse SDRF files and catch errors instantly is using an automated validation tool. It combines structural checks, OLS ontology lookup, and consistency analysis to identify issues across columns and values.

Do I need the parse_sdrf tool to validate proteomics SDRF datasets?

Yes, validating proteomics SDRF datasets requires the parse_sdrf tool along with OLS ontology lookup and SDRF specification data. These dependencies are necessary to perform comprehensive checks across columns and ontology terms.