AnnData agent skills for Claude Code, Codex and other agents.

Walks through single-cell RNA-seq analysis with Scanpy: loading .h5ad and 10X data, QC, normalization, PCA and UMAP, Leiden clustering, marker genes and cell type annotation.

davila7/claude-code-templates

Run AlphaGenome-PyTorch to get genomic track predictions — via the agt predict CLI (single locus, BED regions, whole chromosomes, raw FASTA sequences, or per-gene count tables/AnnData), variant…

genomicsxai/alphagenome-pytorch

Embed and annotate single-cell expression data with scGPT, a foundation model for single-cell biology.

JimLiu/science-skills

Runs differential gene expression analysis on bulk RNA-seq counts with PyDESeq2: design formulas, Wald tests, FDR correction and volcano or MA plots.

davila7/claude-code-templates

This skill should be used when working with annotated data matrices in Python, particularly for single-cell genomics analysis, managing experimental measurements with metadata, or handling…

davila7/claude-code-templates

Runs a seven-step quality-control and exploration pipeline on scRNA-seq, CyTOF or flow cytometry data and writes a plain-language report of what it found.

LigphiDonk/Oh-my--paper

Single-cell RNA-seq data preparation and quality control pipeline.

harrisongzhang/TheVirtualBiotech

Atera platform branch of the spatial transcriptomics workflow — load and validate Atera cell-level output (AnnData + Zarr segmentation) for downstream analysis.

QING1105/ezST

Supports audited local Geniml genomic-interval workflows: validate BED and universe contracts, plan Region2Vec or scEmbed runs, inspect model/tokenizer compatibility, and assess consensus universes.

K-Dense-AI/scientific-agent-skills

Performs Scanpy single-cell RNA-seq QC, normalization, HVG selection, PCA/UMAP/t-SNE, clustering, exploratory marker ranking, pseudobulk preparation, visualization, and Seurat or…

K-Dense-AI/scientific-agent-skills

Queries the CZ CELLxGENE Census programmatically for versioned public single-cell and spatial transcriptomics data.

K-Dense-AI/scientific-agent-skills

Manages biological datasets and models with LaminDB, including artifact registration, lineage tracking, schema validation, Bionty ontology annotation, query/search, collections, branches, storage…

K-Dense-AI/scientific-agent-skills

Omics and single-cell workflow guide for AnnData, Scanpy-style dataset profiling, PyDESeq2-oriented count checks, pysam alignment inspection, and pyOpenMS mass-spectrometry summaries.

DrugClaw/DrugClaw

Annotated matrices for single-cell genomics. An agent skill from jaechang-hits/SciAgent-Skills.

jaechang-hits/SciAgent-Skills

Handles annotated matrices in single-cell analysis, .h5ad and Zarr files, and integration with the scverse ecosystem.

K-Dense-AI/scientific-agent-skills

Local scVI/scANVI-based single-cell latent embedding and batch-aware integration from raw-count .h5ad or 10x Matrix Market input, with stable integrated AnnData export for downstream latent analysis.

ClawBio/ClawBio

Stage 1 of the spatial transcriptomics workflow — load 10x Visium data and QC-filter low-quality spots.

QING1105/ezST

Read, write, and create single-cell data objects using Seurat (R) and Scanpy (Python).

FreedomIntelligence/OpenClaw-Medical-Skills

Load spatial transcriptomics data from Visium, Xenium, MERFISH, Slide-seq, and other platforms using Squidpy and SpatialData.

FreedomIntelligence/OpenClaw-Medical-Skills

Differential gene expression analysis for bulk RNA-seq count matrices using a DESeq2-like workflow in Python; use when you need Wald tests, FDR correction, and optional LFC shrinkage for…

aipoch/medical-research-skills

Reads, inspects, and writes Flow Cytometry Standard (FCS) files from conventional, spectral, and mass cytometry (CyTOF), and parses FlowJo/Cytobank/Diva workspaces.

GPTomics/bioSkills

Read, write, create, and convert single-cell objects across AnnData (Python), Seurat (R), and SingleCellExperiment (R).

GPTomics/bioSkills

Loads spatial transcriptomics data from Visium, Visium HD, Xenium, MERFISH/MERSCOPE, CosMx, Slide-seq/Curio, and Stereo-seq into AnnData or SpatialData using spatialdata-io and Squidpy.

GPTomics/bioSkills

Integrates single-cell paired TCR/BCR (10x VDJ, AIRR, dandelion, BD Rhapsody) with gene expression in an AnnData/MuData object using scirpy - chain-pairing QC, clonotype definition, clonal…

GPTomics/bioSkills

Automated scRNA-seq cell type annotation via pre-trained logistic regression.

jaechang-hits/SciAgent-Skills

Guide Claude through ingesting TCGA sample sheets, expression archives, and clinical carts into omicverse, initialising survival metadata, and exporting annotated AnnData files.

FreedomIntelligence/OpenClaw-Medical-Skills

Open-source FAIR biology data framework. An agent skill from jaechang-hits/SciAgent-Skills.

jaechang-hits/SciAgent-Skills

Standard single-cell RNA-seq analysis pipeline. An agent skill from aipoch/medical-research-skills.

aipoch/medical-research-skills

Map scRNA-seq atlases onto spatial transcriptomics slides using omicverse's Single2Spatial workflow for deep-forest training, spot-level assessment, and marker visualisation.

majiayu000/claude-skill-registry

Imports gene expression count matrices from featureCounts, HTSeq, STAR ReadsPerGene, Salmon/kallisto via tximport or tximeta, RSEM, 10X Genomics MTX/H5, AnnData H5AD, and RDS.

GPTomics/bioSkills

Stores and operates on sparse expression matrices for single-cell and large bulk RNA-seq, covering dgCMatrix/dgRMatrix/dgTMatrix when-each-is-fast, the dgCMatrix (CSC, R) <- CSR (Python) implicit…

GPTomics/bioSkills

Deep generative models for single-cell omics; use when you need probabilistic batch correction (scVI), transfer learning, uncertainty-aware differential expression, or multimodal integration…

aipoch/medical-research-skills

Data structure for annotated matrices in single-cell analysis; use when reading/writing .h5ad (or zarr) and exchanging data with the scverse ecosystem.

aipoch/medical-research-skills

Multi-Omics Factor Analysis v2 (MOFA+) with mofapy2. An agent skill from jaechang-hits/SciAgent-Skills.

jaechang-hits/SciAgent-Skills

Multi-modal single-cell analysis with muon/MuData. An agent skill from jaechang-hits/SciAgent-Skills.

jaechang-hits/SciAgent-Skills

Deep generative models for single-cell omics: probabilistic batch correction (scVI), semi-supervised annotation (scANVI), CITE-seq RNA+protein (totalVI), transfer learning (scARCHES), and DE with…

jaechang-hits/SciAgent-Skills

Use omicverse's pyComBat wrapper to remove batch effects from merged bulk RNA-seq or microarray cohorts, export corrected matrices, and benchmark pre/post correction visualisations.

majiayu000/claude-skill-registry

Run omicverse's CellPhoneDB v5 wrapper on annotated single-cell data to infer ligand-receptor networks and produce CellChat-style visualisations.

majiayu000/claude-skill-registry

Load when you want a verified multi-method consensus over spatial tissue domains on a preprocessed spatial AnnData — fanning out N domain methods, ranking base clusterings, and emitting a typed…

TianGzlab/OmicsClaw

Load when integrating multi-sample scRNA-seq with Harmony, scVI, scANVI, BBKNN, Scanorama, SIMBA, or supported R-backed methods to remove batch effects.

TianGzlab/OmicsClaw

Load when assigning cell-type labels to a clustered scRNA AnnData via marker dictionaries, CellTypist, PopV, KNNPredict, SingleR, scmap, SCSA, or a manual cluster-to-label map.

TianGzlab/OmicsClaw

Load when computing cell-cell ligand-receptor communication on an annotated scRNA AnnData via builtin scorer, LIANA, CellPhoneDB, CellChat (R), or NicheNet (R).

TianGzlab/OmicsClaw

Load when building the neighbour graph, embedding (UMAP/t-SNE/diffmap/PHATE), and clustering (Leiden/Louvain) on a normalised single-cell AnnData.

TianGzlab/OmicsClaw

Load when you want resolution-robust single-cell clusters on a preprocessed scRNA AnnData — fanning out leiden/louvain across a resolution sweep, scoring members by silhouette + cross-method NMI…

TianGzlab/OmicsClaw

Load when turning scRNA FASTQ (or existing CellRanger/STARsolo/SimpleAF/kb-python output) into a downstream-ready AnnData.

TianGzlab/OmicsClaw

Load when computing per-cell differentiation potency / stemness scores from gene-expression complexity on a scRNA AnnData via the CytoTRACE-simple method.

TianGzlab/OmicsClaw

Load when finding marker genes per cluster or comparing condition expression in single-cell RNA-seq.

TianGzlab/OmicsClaw

Load when testing whether cell-type / cluster proportions or neighbourhood densities differ between conditions in a multi-sample scRNA AnnData via Milo, scCODA, simple proportion screen, or R…

TianGzlab/OmicsClaw