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Data & AI AI Skills

Discover Data & AI skills, compare source repositories and read the instructions for each workflow.

Categories: Data & AI

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GitHub

scvitools

Ketomihine

Comprehensive skill for scvi-tools - Deep probabilistic models for single-cell omics analysis. Use for scVI, scANVI, totalVI, MultiVI models, single-cell RNA-seq integration, batch correction, differential expr...

GitHub

cellphonedb

Ketomihine

Comprehensive skill for CellPhoneDB - Database of cell type markers and cell-cell communication analysis for single-cell data. Use for cell type annotation, ligand-receptor analysis, cell-cell interaction infer...

GitHub

spatialdata-io-docs-local

Ketomihine

SpatialData-IO 空间数据I/O工具包 - 100%覆盖85个核心文件(20个空间平台+6个API+6个CLI+53个生成器文档)

GitHub

archr-local

Ketomihine

ArchR docs served from downloaded_docs/archr_scrape - comprehensive scATAC-seq analysis toolkit with all HTML files explicitly listed

GitHub

seurat-local

Ketomihine

Seurat 文档本地镜像(Satija Lab docs,包含 seurat/ 子目录与 archive)

GitHub

scvitools-complete

Ketomihine

scvi-tools 单细胞深度学习框架 - 100%覆盖文档(完整API+教程+模型训练+多模态整合)

GitHub

giottosuite-local

Ketomihine

Giotto Suite 空间转录组学分析工具包 - 100%覆盖718个文件(37个教程+285个核心API+4个数据文档+344个扩展函数+49个工具函数)

GitHub

mudata-complete

Ketomihine

MuData 多模态数据分析工具包 - 100%覆盖文档(API+教程+IO指南+核心功能)

GitHub

envi-pkg-local

Ketomihine

ENVI 本地文档+教程+源码 - 100%覆盖13个文件(Sphinx build + 手动转换 notebooks/py)

GitHub

liana-py-complete

Ketomihine

Liana-py 细胞互作分析工具包 - 100%覆盖文档(60个核心文件+72个图片文件)

GitHub

scglue-complete

Ketomihine

scGLUE 单细胞多组学数据整合工具包 - 100%覆盖文档(完整API+教程+数据整合+图谱分析)

GitHub

scanpy-complete

Ketomihine

Scanpy 单细胞分析工具包 - 100%覆盖文档(API+教程+预处理+分析+可视化)

GitHub

spatialdata

Ketomihine

SpatialData framework for handling spatial multi-modal and multi-scale data. Use this skill for questions about spatial omics data analysis, visualization, and the SpatialData Python ecosystem.

The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.

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