sc-best-practices-auto

Structure best-practice workflows for single-cell AIRR analysis with Python packages.

1|Updated Dec 3, 2025

Search Tags:#multimodal #single-cell #annotation #scRNA-seq #AIRR #clonotype

Ready to run

npx skills add https://github.com/Ketomihine/my_skills --skill sc-best-practices-auto

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: sc-best-practices-auto
Source: https://github.com/Ketomihine/my_skills/tree/main/sc-best-practices-auto
Command: npx skills add https://github.com/Ketomihine/my_skills --skill sc-best-practices-auto

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill consolidates best-practice guidance for single-cell AIRR (TCR/BCR) analysis, turning scattered notes into a coherent workflow that researchers can follow to reproduce and adapt.

Core Features & Use Cases

Automated annotation guidance for immune-receptor data linked to transcriptomes.
Clonotype and repertoire analysis workflows covering diversity, abundance, and network visualizations.
Multimodal integration examples combining GEX, TCR/BCR data, and downstream analyses for end-to-end insights.
Use Case: Reproduce tutorial analyses on a new AIRR dataset and tailor pipelines for your study.

Quick Start

Load your AIRR and scRNA-seq data, then follow the references to apply annotation, clonotype, and multimodal analysis steps described in the material.

Frequently Asked Questions about sc-best-practices-auto

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

FAQPage Schema

How do I structure a single-cell AIRR analysis workflow for TCR and BCR data?▼

To structure single-cell AIRR analysis, this Skill provides best-practice workflows covering TCR/BCR processing, annotation, clonotype definition, and diversity metrics. It consolidates scattered notes into a coherent pipeline you can follow to reproduce and adapt for your study.

What is the best way to integrate scRNA-seq data with immune receptor repertoire annotations?▼

The best way to integrate scRNA-seq with immune receptor data is through multimodal integration examples. This Skill guides you in combining GEX and TCR/BCR data using scanpy, scirpy, and anndata for end-to-end downstream insights.

Can I use scanpy and scirpy for clonotype definition and diversity analysis?▼

Yes, you can use scanpy and scirpy for clonotype definition and diversity analysis. This Skill requires these Python packages and provides workflow references covering abundance, diversity metrics, and network visualizations for AIRR datasets.

Do I need anndata to reproduce single-cell AIRR tutorial pipelines?▼

Yes, you need anndata to reproduce single-cell AIRR tutorial pipelines. The Skill implementation requires Python packages such as scanpy, scirpy, and anndata, relying on references and tutorials to ensure reproducibility across scRNA-seq datasets.

How do I automate annotation guidance for immune receptor data linked to transcriptomes?▼

To automate annotation guidance for immune receptor data linked to transcriptomes, load your AIRR and scRNA-seq data and follow the Skill's references. These apply annotation, clonotype, and multimodal analysis steps described in the consolidated material.