data science & ml Claude 스킬을 발견하세요. 53개의 스킬을 탐색하고 AI 워크플로우에 완벽한 기능을 찾아보세요.
Processes and analyzes complex physiological signals including ECG, EEG, and EDA using a comprehensive Python toolkit.
Performs constraint-based reconstruction and analysis of metabolic models using Python for systems biology and metabolic engineering.
Empowers protein research and design using state-of-the-art ESM3 and ESM C language models for sequence generation, structure prediction, and representation learning.
Generates interactive, publication-quality data visualizations and dashboards using the Plotly Python library.
Infers gene regulatory networks from transcriptomics data using scalable gradient boosting and random forest algorithms.
Analyzes high-throughput sequencing data to perform quality control, normalization, and publication-quality visualization for NGS experiments.
Provides high-performance tools for genomic interval analysis, overlap detection, and machine learning preprocessing using Rust and Python.
Integrates managed vector databases into AI applications for production-grade RAG, semantic search, and recommendation systems.
Simulates high-performance fluid dynamics using pseudospectral methods and Python-based HPC workflows.
Automates complex biomedical research tasks including genomics, drug discovery, and clinical data analysis using an autonomous AI agent framework.
Accesses the European Nucleotide Archive (ENA) to retrieve genomic sequences, raw reads, and metadata for bioinformatics pipelines.
Predicts high-accuracy 3D protein-ligand binding poses using diffusion-based deep learning for structure-based drug design.
Accesses and queries the NCBI Gene database to retrieve comprehensive genetic information, sequences, and functional annotations via E-utilities and Datasets APIs.
Processes digital pathology whole slide images by automating tissue detection, tile extraction, and preprocessing for machine learning pipelines.
Accelerates genomic interval analysis and machine learning preprocessing using a high-performance Rust-based toolkit with Python bindings.
Processes and generates audio, video, images, and complex documents using Google Gemini's advanced multimodal API capabilities.
Infers gene regulatory networks (GRNs) from transcriptomics data using scalable algorithms like GRNBoost2 and GENIE3.
Accesses and analyzes gene expression and functional genomics data from the NCBI Gene Expression Omnibus repository.
Develops and deploys specialized machine learning models for healthcare using clinical datasets and medical coding systems.
Automates the migration of legacy PyTorch AT_DISPATCH macros to the modern AT_DISPATCH_V2 API.
Facilitates mass spectrometry data analysis using the pyOpenMS library for proteomics and metabolomics workflows.
Provides programmatic access to openFDA datasets for drug safety research, medical device surveillance, and food recall analysis.
Accesses the Human Metabolome Database to retrieve metabolite properties, clinical biomarker data, and spectral information for metabolomics research.
Simulates complex fluid dynamics using high-performance Python pseudospectral methods for Navier-Stokes and geophysical flow equations.
Simplifies the creation, manipulation, and analysis of complex networks and graph data structures in Python.
Automates complex scientific research workflows and tool discovery across bioinformatics, genomics, and drug discovery domains.
Queries and analyzes SEC filings and financial statements for deep financial research and company data extraction.
Integrates Google's Gemini models into your terminal workflow for advanced code analysis and complex multi-model reasoning.
Generates interactive, publication-quality scientific and statistical visualizations using the Plotly Python library.
Simplifies high-performance computational fluid dynamics (CFD) simulations and analysis using Python and pseudospectral methods.
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