Explore AI Agent Skills & Claude Prompts

Query KEGG database for drug information, pathway analysis, and disease-drug-target discovery. Use this skill when: (1) Looking up drug information including efficacy, targets, metabolism, and interactions, (2) Analyzing metabolic or signaling pathways to retrieve genes, compounds, and modules, (3) Discovering disease-associated drugs, genes, and pathways for drug repurposing.

molecule-biochemical-significance-query-biot5

Query a molecule's biochemical significance and roles in biology and chemistry using BioT5 multi-modal model. Use this skill when: (1) Understanding a molecule's biological roles and functions, (2) Describing a molecule's chemical significance and applications, (3) Getting natural language explanations of molecular properties, (4) Summarizing what a molecule is used for or its metabolic relevance.

pubchem-query

Query PubChem database for chemical structures, similar compounds, and bioactivity data. Use this skill when: (1) Converting drug name to molecular structure (SMILES, SDF), (2) Finding similar compounds for lead optimization, (3) Querying bioactivity data against protein targets, (4) Getting compounds active in specific assays.

cellxgene-census-query

Query CZ CELLxGENE Census (61M+ cells). Filter by cell type/tissue/disease, retrieve expression data, and integrate with scanpy/PyTorch for population-scale single-cell analysis. Use this skill when: (1) Querying single-cell expression data by cell type, tissue, or disease, (2) Exploring available single-cell datasets and metadata, (3) Training machine learning models on single-cell data, (4) Performing large-scale cross-dataset analyses.

chembl-query

Query ChEMBL database for bioactivity data on drug-like compounds. Use this skill when: (1) Finding compounds active against a protein target (target-based search), (2) Getting bioactivity profile for a molecule (molecule-based search), (3) Finding drugs for a disease indication (indication-based search).

ppi-string-query

Query STRING database for protein-protein interactions with confidence scores. Use this skill when: (1) Finding interaction partners for a protein of interest, (2) Retrieving confidence scores for protein-protein interactions, (3) Building protein interaction networks for pathway analysis.

single-cell-foundation-model-geneformer

Use this skill when a task involves Geneformer workflows, especially TranscriptomeTokenizer input preparation, tokenized `.dataset` generation, cell or gene classification with `Classifier`, embedding extraction with `EmbExtractor`, and in silico perturbation analysis with `InSilicoPerturber`.

single-cell-foundation-model-langcell

Use this skill when a task involves the LangCell project for single-cell language-cell modeling, especially zero-shot cell type annotation, few-shot annotation, LangCell-CE finetuning, Geneformer-style tokenization, or preparing text descriptions for candidate cell identities and multimodal cell-text matching workflows.

single-cell-foundation-model-scgpt

Use this skill when a task involves the local scGPT project in /DATA/disk0/zhaosy/home/scGPT, especially scGPT preprocessing and binning, checkpoint vocabulary matching, cell embedding extraction, reference mapping, fine-tuning scGPT for integration or annotation, or using scGPT tutorials for GRN, perturbation, multiomics, and reference mapping workflows.

single-cell-foundation-model-stofm

Use this skill when a task involves the local SToFM project in /DATA/disk0/zhaosy/home/SToFM, especially preprocessing spatial transcriptomics data for SToFM, generating cell embeddings with the cell encoder plus SE(2) Transformer pipeline, handling spatial coordinates, or preparing SToFM embeddings for downstream region segmentation or cell type annotation.

single-cell-scrna-seq-analysis-scanpy

Complete single-cell RNA-seq analysis workflow built on Scanpy and AnnData. Use this skill when: (1) Loading diverse single-cell data formats (10X, h5ad, CSV), (2) Performing quality control and filtering, (3) Normalization, dimensionality reduction, and clustering, (4) Marker gene identification and cell type annotation.

single-cell-multi-omics-analysis-scvi

Probabilistic deep learning framework for single-cell multi-omics data analysis. Use this skill when: (1) Analyzing single-cell RNA-seq data with batch correction, (2) Integrating multi-modal data (CITE-seq, ATAC-seq, multi-omics), (3) Performing cell type annotation with scANVI, (4) Spatial transcriptomics deconvolution with DestVI.