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bio-stats-ml-reporting

Automates end-to-end ML reporting for omics analysis using DuckDB, scikit-learn, and XGBoost.

7|1|Updated Feb 2, 2026
Search Tags:#reporting#references#duckdb#omics#ml#scikit-learn#xgboost
One-click install
npx skills add https://github.com/fmschulz/omics-skills --skill bio-stats-ml-reporting
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bio-stats-ml-reporting
Source: https://github.com/fmschulz/omics-skills/tree/main/skills/bio-stats-ml-reporting
Command: npx skills add https://github.com/fmschulz/omics-skills --skill bio-stats-ml-reporting

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill centralizes the collection, modeling, and reporting of omics results by integrating data from feature tables, model outputs, and literature references to deliver reproducible analyses and publication-ready reports.

Core Features & Use Cases

  • Aggregate results from DuckDB feature tables and metadata into a unified analysis dataset.
  • Train baseline and advanced ML models (scikit-learn and XGBoost) with cross-validation, evaluate performance, and export models, metrics, and visualizations.
  • Generate publication-ready reports with validated references and auto-generated citations for downstream dissemination.

Quick Start

Run the end-to-end workflow to aggregate results, train models, and generate the report.

Frequently Asked Questions about bio-stats-ml-reporting

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

FAQPage Schema
How do I automate ML reporting for omics data from model outputs to publication-ready insights?▼

Automating ML reporting for omics data involves aggregating feature tables and model outputs into DuckDB, training models with scikit-learn and XGBoost, and generating validated reports with auto-citations.

Can I use scikit-learn and XGBoost for cross-validation on DuckDB feature tables?▼

Yes, you can use scikit-learn and XGBoost for cross-validation on DuckDB feature tables. The workflow aggregates results and trains baseline and advanced models while exporting metrics and visualizations.

How do I generate publication-ready reports with validated references from omics pipelines?▼

Generating publication-ready reports from omics pipelines requires integrating model outputs with Crossref-based validation. This process produces auto-generated citations and validated references for downstream dissemination.

What is the best way to centralize omics results and metadata for reproducible analysis?▼

Centralizing omics results requires integrating feature tables, model outputs, and literature references into a unified DuckDB dataset. This approach delivers reproducible analyses and publication-ready reports.

Does the ML reporting workflow require external dependencies for reference validation?▼

The ML reporting workflow uses Crossref-based validation for reference checking. It operates without external dependencies to produce validated reports and auto-generated citations from your omics data.

Why use DuckDB for data integration in omics machine learning pipelines?▼

DuckDB is used for data integration in omics pipelines to aggregate feature tables and metadata into a unified analysis dataset. This enables seamless training and evaluation of machine learning models.

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