archr-local

Provide offline ArchR documentation for scATAC-seq analysis workflows.

1|Updated Dec 3, 2025
One-click install
npx skills add https://github.com/Ketomihine/my_skills --skill archr-local
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: archr-local
Source: https://github.com/Ketomihine/my_skills/tree/main
Command: npx skills add https://github.com/Ketomihine/my_skills --skill archr-local

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides comprehensive, offline ArchR documentation for single-cell ATAC-seq analysis, hosted locally to enable quick reference without internet access.

Core Features & Use Cases

  • Reference documentation: Access core ArchR topics such as data preparation, dimensionality reduction, clustering, motif analysis, and multiome workflows directly from the repository.
  • Self-contained learning: Ideal for researchers and students who need offline guidance and reproducible examples.
  • Use Case: A lab team can train new analysts using the included chapters like data_preparation.md, dimensionality_reduction.md, and enrichment_analysis.md without external connectivity.

Quick Start

Browse the archr-local/references directory and start with data_preparation.md to learn how ArchR handles input formats and project setup. Then explore dimensionality_reduction.md and clustering.md for common workflows.

Frequently Asked Questions about archr-local

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

FAQPage Schema
How do I access ArchR documentation for scATAC-seq analysis without an internet connection?▼

You can access offline ArchR documentation by browsing the references directory, which provides self-contained chapters on scATAC-seq topics like data preparation, dimensionality reduction, and clustering without requiring internet connectivity.

What topics are covered in offline ArchR reference materials?▼

The offline ArchR references cover data preparation, dimensionality reduction, clustering, motif annotation, enrichment analysis, and multiome workflows, providing practical examples and best practices for scATAC-seq analysis.

Can I use this offline ArchR documentation to train new analysts in a lab environment?▼

Yes, lab teams can use the self-contained documentation chapters, such as data_preparation.md and dimensionality_reduction.md, to train new analysts in scATAC-seq workflows without needing external network connectivity.

Where should I start when learning ArchR workflows for single-cell ATAC-seq?▼

Start by reading the data_preparation.md file to understand input formats and project setup, then proceed to dimensionality_reduction.md and clustering.md to learn common scATAC-seq analysis workflows.

Does the offline ArchR documentation include guidance for multiome workflows?▼

Yes, the offline documentation includes reference materials and practical guidance for multiome workflows alongside standard scATAC-seq topics like motif annotation and enrichment analysis.