omics-general

Standardize bioinformatics workflows using AnnData and MuData containers.

29|3|Updated Jun 11, 2026
One-click install
npx skills add https://github.com/inflexa-ai/inflexa --skill omics-general
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: omics-general
Source: https://github.com/inflexa-ai/inflexa/tree/main/skills/shared/omics-general
Command: npx skills add https://github.com/inflexa-ai/inflexa --skill omics-general

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the lack of consistency in bioinformatics analysis by providing a unified framework for data handling, language selection, and analytical methodology across diverse omics modalities.

Core Features & Use Cases

  • Universal Data Containers: Enforces the use of AnnData and MuData formats to ensure metadata integrity and interoperability across Python and R ecosystems.
  • Standardized Analysis Pipelines: Provides a structured approach to data ingestion, QC, preprocessing, and downstream interpretation.
  • Use Case: A researcher needs to integrate single-cell RNA-seq data with proteomics. This Skill provides the specific conventions for AnnData/MuData conversion and the recommended cross-cutting methods for differential analysis and pathway enrichment.

Quick Start

Use the omics-general skill to initialize a standard analysis plan for a new bulk RNA-seq dataset using AnnData containers.

Frequently Asked Questions about omics-general

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

FAQPage Schema
How do I standardize bioinformatics workflows for multi-omics integration using Python?▼

Standardizing multi-omics integration involves enforcing AnnData and MuData container usage to ensure metadata integrity and applying consistent cross-platform methodology for Python-first analysis workflows.

What is the best way to integrate single-cell RNA-seq data with proteomics reproducibly?▼

Integrating single-cell RNA-seq with proteomics reproducibly requires using MuData containers for cross-modal data handling and applying standardized conventions for downstream differential analysis and functional interpretation.

Does this reproducible omics approach support legacy R bioinformatics tools?▼

Yes, the reproducible omics approach supports legacy R bioinformatics tools by requiring adherence to Python-first language policies and applying specific R-bridge conventions for interoperability.

How do I initialize a standard bulk RNA-seq analysis plan using AnnData containers?▼

To initialize a standard bulk RNA-seq analysis plan, use the omics framework to generate a structured data ingestion, QC, and preprocessing pipeline that strictly enforces AnnData container formats.

Can I use MuData formats for differential expression analysis and pathway enrichment?▼

Yes, you can use MuData formats for differential expression analysis and pathway enrichment, as the framework provides specific conventions and recommended cross-cutting methods for these downstream functional interpretation tasks.