data science & ml Claude 스킬을 발견하세요. 61개의 스킬을 탐색하고 AI 워크플로우에 완벽한 기능을 찾아보세요.
Generates publication-quality scientific diagrams, neural network architectures, and flowcharts using specialized Python libraries.
Accesses the Reactome database to perform biological pathway analysis, gene mapping, and enrichment studies for systems biology.
Facilitates programmatic access to the ClinicalTrials.gov API v2 for advanced trial discovery, patient matching, and medical research data extraction.
Builds and deploys production-grade bioinformatics pipelines as serverless workflows on the Latch platform.
Facilitates creative scientific problem-solving by generating hypotheses and exploring interdisciplinary connections as a research ideation partner.
Searches and retrieves life sciences preprints from the bioRxiv database by keywords, authors, and categories.
Accesses and analyzes over 240 million scholarly works, authors, and institutions via the OpenAlex API for automated scientific discovery.
Empowers Claude to perform graph-based drug discovery, molecular property prediction, and protein modeling using the TorchDrug framework.
Access and retrieve comprehensive nucleotide sequence data and metadata from the European Nucleotide Archive (ENA).
Infers gene regulatory networks from transcriptomics data using scalable algorithms like GRNBoost2 and GENIE3.
Enables advanced searching and programmatic retrieval of biomedical literature from the PubMed database using E-utilities and MeSH terms.
Provides comprehensive tools for materials analysis, crystal structure manipulation, and Materials Project database integration.
Accesses, searches, and retrieves gene expression and functional genomics data from the NCBI Gene Expression Omnibus (GEO).
Analyzes and visualizes complex graph data structures using the comprehensive Python NetworkX library.
Generates professional, publication-ready clinical decision support documents and biomarker-stratified cohort analyses in LaTeX format.
Integrates comprehensive pharmaceutical data from DrugBank for drug discovery, pharmacology research, and safety analysis.
Processes and analyzes complex mass spectrometry data for proteomics and metabolomics workflows using the PyOpenMS library.
Manages and analyzes annotated data matrices for single-cell genomics and large-scale biological datasets.
Processes and analyzes high-throughput sequencing data to generate publication-quality genomic visualizations and quality control reports.
Retrieves genomic, proteomic, and structural data from over 20 biological databases using a unified interface.
Applies medicinal chemistry filters and drug-likeness rules to prioritize compound libraries for autonomous discovery.
Accelerates high-performance data analysis and manipulation using the lightning-fast Polars DataFrame library.
Processes and analyzes massive tabular datasets exceeding available RAM using lazy, out-of-core DataFrame operations.
Performs exact symbolic mathematics in Python, including algebraic solving, calculus, and matrix manipulations.
Models complex systems using process-based discrete-event simulation to optimize resources, queues, and time-based workflows.
Conducts systematic, rigorous reviews of scientific manuscripts and grant proposals by evaluating methodology, statistics, and reporting standards.
Automates electronic lab notebook workflows by providing programmatic access to LabArchives for research data management and documentation.
Accesses and orchestrates over 600 scientific tools and databases for bioinformatics, drug discovery, and life sciences research workflows.
Aggregates and synthesizes perspectives from multiple AI agents to provide comprehensive, consensus-driven answers to complex queries.
Aggregates and synthesizes diverse perspectives from multiple AI agents to provide a consensus-driven answer to complex queries.
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