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。
Generates publication-quality statistical graphics and exploratory data visualizations using the Seaborn Python library.
Accesses and queries the world's largest chemical database for compound information, molecular properties, and bioactivity data.
Simplifies complex time series machine learning tasks including forecasting, classification, and anomaly detection using a scikit-learn compatible toolkit.
Searches and retrieves metadata and full-text PDFs from the bioRxiv preprint server for life sciences research discovery.
Provides programmatic access to standardized single-cell genomics data for large-scale analysis and machine learning.
Simulates complex fluid dynamics using high-performance Python-based pseudospectral methods for scientific research.
Automates electronic lab notebook workflows by managing entries, attachments, and backups through the LabArchives REST API.
Detects and mitigates cognitive biases to improve decision-making, forecasting accuracy, and intellectual honesty.
Provides programmatic access to comprehensive pharmacogenomics data for precision medicine, clinical guidelines, and gene-drug interaction analysis.
Verifies that machine learning models correctly respect intended symmetries through systematic numerical tests and debugging guidance.
Builds and manages process-based discrete-event simulations in Python to model complex systems like manufacturing, logistics, and network traffic.
Develops and trains Graph Neural Networks (GNNs) for complex data structures like social networks, molecules, and 3D point clouds.
Systematically investigates causal relationships to identify fundamental root causes and distinguish them from symptoms or correlations.
Facilitates advanced biomedical literature research and programmatic access to the National Library of Medicine's PubMed database via E-utilities.
Accesses ChEMBL's vast repository of bioactive molecules and drug discovery data for medicinal chemistry and pharmacology research.
Analyzes and processes high-performance genomic interval data for computational biology and machine learning workflows.
Implements and fine-tunes state-of-the-art machine learning models for natural language processing, computer vision, and audio tasks.
Powers protein engineering workflows with state-of-the-art generative design, structure prediction, and high-quality sequence embeddings.
Accesses and retrieves nucleotide sequence data, raw reads, and genome assemblies from the European Nucleotide Archive.
Provides unified, high-speed access to 20+ genomic databases and protein structure prediction tools directly from the command line.
Analyzes single-cell omics data using deep generative models and probabilistic frameworks for batch correction and multimodal integration.
Evaluates research rigor, methodology, and statistical validity to critically analyze scientific claims and evidence quality.
Manages large-scale N-dimensional arrays with efficient chunking, compression, and cloud-native storage integration.
Accesses the Human Metabolome Database (HMDB) to retrieve detailed information on over 220,000 human metabolites, including chemical properties, clinical biomarkers, and spectral data.
Performs rigorous statistical modeling, econometric analysis, and hypothesis testing using Python's premier inference toolkit.
Builds custom, interactive data-driven visualizations using D3.js for complex layouts, maps, and bespoke charting requirements.
Provides comprehensive tools for astronomical data analysis, coordinate transformations, and physical unit conversions in Python.
Converts diverse file formats including PDFs, Office documents, and media into LLM-optimized Markdown for AI processing.
Queries and retrieves comprehensive genetic data from NCBI using E-utilities and Datasets APIs for biological research and gene annotation.
Automates laboratory liquid handling and equipment control through a hardware-agnostic Python interface.
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