发现data science & ml类别的 Claude 技能。浏览 53 个技能,找到适合您 AI 工作流程的完美功能。
Develops and optimizes quantum circuits, hybrid quantum-classical models, and molecular simulations using the PennyLane library.
Runs Python code in the cloud with serverless containers, autoscaling GPUs, and minimal infrastructure management.
Accesses AI-ready Therapeutics Data Commons (TDC) datasets and benchmarks for drug discovery and pharmaceutical machine learning.
Optimizes data processing workflows with high-performance Polars expressions, lazy evaluation, and efficient DataFrame manipulations.
Empowers Claude with expert capabilities for manipulating, analyzing, and visualizing geospatial vector data using Python.
Builds, optimizes, and executes quantum circuits and algorithms on simulators or real hardware using the Qiskit framework.
Queries the NHGRI-EBI GWAS Catalog to retrieve genetic variant associations, study metadata, and genomic summary statistics.
Builds, analyzes, and visualizes complex networks and graph data structures using the comprehensive NetworkX library for Python.
Processes and analyzes high-performance genomic interval data for computational biology and machine learning applications.
Performs advanced molecular analysis, manipulation, and chemical informatics tasks using the RDKit toolkit.
Enables parallel and distributed computing in Python to scale data processing beyond memory limits.
Solves complex single and multi-objective optimization problems using evolutionary algorithms and Pareto front analysis.
Simplifies molecular cheminformatics workflows by providing a Pythonic wrapper for RDKit with sensible defaults and parallel processing.
Parses and manipulates Flow Cytometry Standard (FCS) files, converting biological data into NumPy arrays and CSV formats for scientific analysis.
Generates publication-quality statistical graphics and complex data visualizations using the Seaborn Python library.
Performs comprehensive differential gene expression analysis from bulk RNA-seq count data using the Python implementation of DESeq2.
Performs comprehensive single-cell RNA-seq analysis workflows including quality control, normalization, clustering, and cell-type annotation.
Builds, fits, and validates robust Bayesian models using PyMC's modern probabilistic programming interface.
Provides unified, rapid access to over 20 genomic databases and bioinformatics analysis tools for DNA and protein research.
Manipulates genomic datasets including SAM, BAM, VCF, and FASTA files through a Pythonic interface to htslib.
Access and analyze comprehensive FDA regulatory data, including drug safety, medical device clearances, and food recalls through the openFDA API.
Provides a comprehensive toolkit for rigorous statistical modeling, econometric analysis, and time-series forecasting within the Claude environment.
Accesses over 230 million purchasable chemical compounds for virtual screening, drug discovery, and molecular docking studies.
Manages large-scale N-dimensional arrays with chunked storage, compression, and cloud-native parallel I/O.
Integrates NCBI Gene data access into Claude for querying sequences, functional annotations, and genomic metadata.
Enables programmatic access to the RCSB Protein Data Bank for searching, retrieving, and analyzing 3D structures of biological macromolecules.
Simplifies graph-based machine learning for drug discovery, protein modeling, and molecular science using the TorchDrug framework.
Simulates high-performance computational fluid dynamics using pseudospectral methods and Python-based analysis.
Access and retrieve comprehensive nucleotide sequence data and metadata from the European Nucleotide Archive (ENA) for bioinformatics pipelines.
Builds and deploys serverless bioinformatics pipelines using the Latch SDK and cloud infrastructure.
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