data science & ml向けのClaudeスキルを発見してください。61個のスキルを閲覧し、AIワークフローに最適な機能を見つけましょう。
Transforms academic papers into interactive websites, professional presentation videos, and print-ready conference posters.
Manipulates, analyzes, and visualizes phylogenetic trees with support for evolutionary event detection and NCBI taxonomy integration.
Processes and visualizes high-throughput sequencing data (NGS) including ChIP-seq, RNA-seq, and ATAC-seq workflows.
Evaluates scientific manuscripts and grant proposals using systematic methodology, statistical rigor, and ethical standards.
Accesses over 600 scientific tools and datasets to automate research workflows across bioinformatics, genomics, and drug discovery.
Processes and analyzes physiological signals including ECG, EEG, and EDA for psychophysiology research and clinical applications.
Accesses and queries the Catalogue of Somatic Mutations in Cancer (COSMIC) to retrieve genomic data, mutational signatures, and cancer gene census information.
Enables seamless programmatic access to UniProt for protein sequence retrieval, functional annotation, and biological database ID mapping.
Generates publication-quality static, animated, and interactive visualizations using Python's foundational plotting library.
Streamlines the implementation, evaluation, and deployment of classical machine learning models using the scikit-learn library.
Accesses and processes NCBI Gene Expression Omnibus (GEO) datasets for transcriptomics and functional genomics analysis.
Accesses the Kyoto Encyclopedia of Genes and Genomes (KEGG) REST API for biological pathway analysis and molecular interaction mapping.
Queries the openFDA API to retrieve comprehensive data on drugs, medical devices, foods, and regulatory actions for safety research and analysis.
Queries the NHGRI-EBI GWAS Catalog to retrieve genetic variants, SNP-trait associations, and comprehensive genomic summary statistics.
Implements advanced survival analysis and time-to-event modeling using the scikit-survival library in Python.
Queries and interprets NCBI ClinVar genetic variant data to provide clinical significance classifications and genomic annotations.
Scales Python data workflows across multiple cores and machines for larger-than-RAM datasets using parallel and distributed computing.
Enables high-performance analysis and visualization of tabular datasets with billions of rows using lazy, out-of-core DataFrames.
Provides AI-ready datasets, benchmarks, and molecular oracles for drug discovery and therapeutic machine learning.
Solves complex single and multi-objective optimization problems using evolutionary algorithms and Pareto front analysis.
Processes genomic datasets including sequence alignments, variants, and reference sequences using a Pythonic interface to htslib.
Automates protein sequence optimization and experimental validation through a cloud-based wet-lab platform.
Applies medicinal chemistry filters, drug-likeness rules, and structural alerts for molecular prioritization in drug discovery.
Simplifies molecular featurization by providing access to over 100 pre-trained embeddings and hand-crafted featurizers for machine learning.
Manages annotated data matrices and metadata for single-cell genomics and large-scale biological datasets using the AnnData framework.
Builds and deploys serverless bioinformatics pipelines using the Latch SDK with specialized decorators and cloud data management.
Automates end-to-end scientific research workflows from data-driven hypothesis generation to publication-ready LaTeX manuscripts.
Provides a comprehensive toolkit for creating, manipulating, and analyzing complex network structures and graph algorithms in Python.
Implements reinforcement learning workflows including agent training, custom environment design, and model evaluation using Stable Baselines3.
Simplifies molecular machine learning and drug discovery tasks using the DeepChem toolkit for property prediction and GNNs.
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