data science & ml Claude 스킬을 발견하세요. 61개의 스킬을 탐색하고 AI 워크플로우에 완벽한 기능을 찾아보세요.
Integrates the Reactome database to perform pathway enrichment, gene-pathway mapping, and molecular interaction analysis for systems biology.
Generates publication-ready scientific figures and multi-panel layouts using Matplotlib, Seaborn, and Plotly.
Streamlines high-density neural recording analysis, spike sorting, and quality metric computation for Neuropixels electrophysiology data.
Performs professional statistical modeling, hypothesis testing, and rigorous assumption verification with publication-ready APA reporting.
Creates, edits, and analyzes Excel spreadsheets with production-grade formulas, industry-standard financial formatting, and automated recalculation.
Enables advanced materials science research through crystal structure manipulation, thermodynamic analysis, and Materials Project database integration.
Simplifies graph-based machine learning for drug discovery, protein modeling, and molecular science using the TorchDrug framework.
Applies advanced machine learning techniques to chemistry, biology, and materials science for molecular property prediction and drug discovery.
Manages medical imaging data by reading, writing, and anonymizing DICOM files while providing guidance on pixel extraction and metadata manipulation.
Generates and tests scientific hypotheses from observational data and research literature to accelerate empirical discovery and predictive modeling.
Analyzes single-cell genomics data using probabilistic deep generative models for tasks like batch correction, cell type annotation, and multi-omic integration.
Generates and designs novel proteins using evolutionary scale language models for sequence, structure, and functional analysis.
Executes exact symbolic mathematics in Python to solve equations, perform calculus, and manipulate algebraic expressions with mathematical precision.
Analyzes whole-slide pathology images and multiparametric imaging data using advanced machine learning, spatial graph construction, and automated preprocessing pipelines.
Accesses and analyzes comprehensive pharmaceutical data from DrugBank including molecular structures, drug interactions, targets, and pharmacological properties.
Performs advanced survival analysis and time-to-event modeling in Python using specialized machine learning techniques for censored data.
Facilitates advanced astronomical research and data analysis using Python for coordinate transformations, FITS file manipulation, and cosmological calculations.
Processes and analyzes physiological signals like ECG, EEG, and EDA to extract research-grade biometrics and psychophysiological insights.
Models complex discrete-event systems where entities interact with shared resources over time using process-based Python simulations.
Queries and analyzes millions of scholarly works, authors, and institutions using the OpenAlex API to facilitate comprehensive bibliometric research.
Explains machine learning model predictions and feature importance using Shapley values for improved transparency and debugging.
Manages and analyzes annotated data matrices for single-cell genomics and large-scale biological datasets in Python.
Detects system hardware capabilities and generates strategic recommendations for optimized scientific computing and data processing tasks.
Automates the end-to-end scientific research lifecycle from data-driven hypothesis generation to the production of publication-ready LaTeX manuscripts.
Designs, simulates, and executes quantum circuits across diverse hardware backends and simulators using Google's open-source framework.
Facilitates machine learning on genomic interval data by training embeddings for regions, single-cell ATAC-seq, and associated metadata.
Implements, fine-tunes, and deploys pre-trained transformer models for natural language processing, computer vision, and audio tasks.
Provides specialized algorithms and workflows for comprehensive time series analysis, including classification, forecasting, and anomaly detection.
Accelerates reinforcement learning workflows through high-performance training, optimized environment vectorization, and seamless multi-agent integration.
Processes and visualizes billion-row tabular datasets exceeding available RAM through lazy, out-of-core DataFrame operations.
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