Data Science & ML Claude 技能

CZ CELLxGENE Census

16

Facilitates programmatic access and analysis of the CZ CELLxGENE Census database containing over 61 million single-cell genomics records.

Matplotlib Visualization Expert

16

Generates professional, publication-quality Python visualizations including line plots, heatmaps, and 3D charts using the Matplotlib library.

ChEMBL Database for Drug Discovery

16

Queries ChEMBL's vast database of bioactive molecules and drug discovery data for medicinal chemistry research.

Pymatgen Materials Science

16

Automates materials science workflows including crystal structure analysis, phase diagrams, and Materials Project integration.

HMDB Database Explorer

16

Accesses the Human Metabolome Database to retrieve detailed chemical, clinical, and biological data for over 220,000 metabolites.

COBRApy Metabolic Modeling

16

Facilitates constraint-based reconstruction and analysis (COBRA) of metabolic models for systems biology and metabolic engineering.

Reactome Database Integration

16

Integrates Reactome's curated pathway database into Claude for advanced systems biology research and gene enrichment analysis.

NCBI GEO Database Explorer

16

Accesses and retrieves gene expression and functional genomics data from the NCBI Gene Expression Omnibus (GEO) repository.

PyMC Bayesian Modeling

16

Builds and validates complex Bayesian models using PyMC's probabilistic programming framework.

Astropy Scientific Computing

16

Performs advanced astronomical data analysis, coordinate transformations, and cosmological calculations using the industry-standard Astropy library.

Multi Source Data Merger

16

Merges heterogeneous data sources into unified datasets using field mappings and priority-based conflict resolution.

UMAP Dimensionality Reduction

16

Performs fast, scalable non-linear dimensionality reduction and manifold learning for high-dimensional data visualization and clustering.

DeepChem Cheminformatics

16

Applies machine learning to chemistry, biology, and materials science to predict molecular properties and design new compounds.

ETE Toolkit for Phylogenetics

16

Manipulates, analyzes, and visualizes phylogenetic and hierarchical trees for biological research and genomic data.

STRING Database Analysis

16

Queries the STRING database to analyze protein-protein interaction networks and perform comprehensive functional enrichment for systems biology.

LabArchives Integration

16

Automates electronic lab notebook management through the LabArchives REST API for programmatic research documentation and data backup.

Molfeat Molecular Featurization

16

Simplifies the conversion of chemical structures into machine learning-ready numerical features using over 100 diverse featurizers.

TorchDrug Scientific Computing

16

Accelerates drug discovery and molecular research by providing specialized tools for graph neural networks, protein modeling, and chemical property prediction.

Scikit-bio Bioinformatics

16

Performs advanced biological data analysis including sequence manipulation, phylogenetic tree construction, and microbial diversity metrics.

LLM Inference Batching Scheduler

16

Optimizes LLM inference request grouping and scheduling to minimize operational costs while satisfying latency and padding constraints.

scikit-survival Analysis

16

Performs comprehensive survival analysis and time-to-event modeling using the scikit-survival Python library.

Metabolomics Workbench Database

16

Accesses the NIH Metabolomics Workbench to query metabolite data, study metadata, and standardized nomenclature for biomarker discovery.

Torch Tensor Parallelism

16

Implements distributed tensor-parallel linear layers in PyTorch to enable training of models that exceed single-device memory limits.

Open Targets Scientific Database

16

Queries the Open Targets Platform to identify therapeutic drug targets, evaluate disease associations, and analyze clinical trial data.

Zarr Python

16

Manages large-scale N-dimensional arrays with chunking and compression for high-performance scientific computing and cloud storage.

Torch Pipeline Parallelism

16

Implements distributed model training by partitioning PyTorch layers across multiple GPUs using pipeline parallelism patterns like AFAB and 1F1B.

Seaborn Statistical Visualization

16

Generates publication-quality statistical graphics and complex multi-panel data visualizations using the Seaborn Python library.

Statsmodels Statistical Analysis

16

Performs rigorous statistical modeling, econometric analysis, and hypothesis testing using Python's statsmodels library.

Data Resharding and Partitioning

16

Reorganizes large-scale datasets into hierarchical directory structures while enforcing strict file size and item count constraints.

Scikit-learn Machine Learning Toolkit

16

Implements comprehensive machine learning workflows including classification, regression, and data preprocessing using the industry-standard Scikit-learn library.