data-provenance

Record computational analysis provenance for ENCODE data workflows.

26|5|Updated Mar 8, 2026

Search Tags:#automation #workflow #audit #provenance #reproducibility #tracking #ENCODE

Ready to run

npx skills add https://github.com/ammawla/encode-toolkit --skill data-provenance-ammawla

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-provenance
Source: https://github.com/ammawla/encode-toolkit/tree/main/plugin/skills/data-provenance
Command: npx skills add https://github.com/ammawla/encode-toolkit --skill data-provenance-ammawla

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill addresses the challenge of documenting every computational step, tool version, and data source, enabling reproducibility and transparent research.

Core Features & Use Cases

Comprehensive Provenance Logging: Automatically records tool versions, reference files, parameters, and timestamps for each operation.
Derived File Tracking: Log and trace the lineage of output files back to source ENCODE experiment accessions.
Reproducibility Support: Generates detailed methods sections and audit trails suitable for publications and data sharing.
Use Case: When analyzing ENCODE data, automatically record each processing step, associated scripts, and references to produce a complete reproducibility report.

Quick Start

Log an analysis step as completed; then, retrieve the full provenance chain for verification and publication purposes.

Frequently Asked Questions about data-provenance

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

FAQPage Schema

How do I track provenance for ENCODE data analysis workflows?▼

Track provenance for ENCODE data by logging tool versions, reference files, parameters, and timestamps for each operation. This captures detailed records of computational analyses to ensure bioinformatics workflows remain fully reproducible and transparent.

What is reproducibility tracking in bioinformatics computational analysis?▼

Reproducibility tracking is documenting every computational step, data source, and tool version used in an analysis. It solves the challenge of transparent research by enabling exact replication of results and generating detailed methods sections for publications.

How do I generate a methods section from my computational analysis steps?▼

Generate a methods section by logging each analysis step as completed, including associated scripts and references. The provenance chain retrieves these detailed processing records to produce a complete reproducibility report suitable for publication.

Can I trace derived output files back to source ENCODE experiment accessions?▼

Yes, you can trace derived output files back to source ENCODE experiment accessions. Derived file tracking logs the lineage of outputs, maintaining a clear connection between processed data and the original experiments for audit trails.

Do I need any specific dependencies to automate my workflow audit trail?▼

No specific dependencies are required to automate your workflow audit trail. The logging mechanism operates independently to enforce rigorous documentation standards for data processing steps without external package prerequisites.