data science & ml Claude 스킬을 발견하세요. 61개의 스킬을 탐색하고 AI 워크플로우에 완벽한 기능을 찾아보세요.
Automates end-to-end scientific research workflows from data analysis and hypothesis generation to publication-ready LaTeX papers.
Facilitates advanced Bayesian statistical modeling in R using Stan-based packages for comprehensive data analysis and inference.
Implements end-to-end machine learning pipelines in R using the tidymodels ecosystem, from data splitting to model deployment.
Simplifies complex bioinformatics workflows in R using Bioconductor for RNA-seq, microarray, and single-cell genomic analysis.
Optimizes machine learning models using comprehensive hyperparameter tuning patterns within the R Tidymodels ecosystem.
Performs fast, scalable nonlinear dimensionality reduction for high-dimensional data visualization, clustering, and feature engineering.
Evaluates machine learning model performance using R's yardstick and tidymodels ecosystem for robust classification and regression analysis.
Performs comprehensive clinical trial design and statistical analysis in R, covering sample size calculation, randomization, and regulatory-compliant modeling.
Analyzes CSV files automatically to generate comprehensive statistical summaries and context-aware visualizations using Python and pandas.
Performs advanced mathematical physics computations, symbolic algebra, and automated theorem proving using the Theory2 suite.
Implements standardized Agent-to-Agent (A2A) protocol executors with production-ready patterns for task management and agent coordination.
Generates publication-ready scientific figures and multi-panel layouts following strict journal specifications for Nature, Science, and Cell.
Performs automated exploratory data analysis and generates comprehensive reports for over 200 scientific file formats.
Generates interactive, publication-quality Python charts and dashboards for data exploration and presentation.
Develops and trains Graph Neural Networks (GNNs) for node classification, link prediction, and geometric deep learning tasks.
Implements and trains advanced reinforcement learning algorithms to create autonomous agents that evolve through experience.
Builds, evaluates, and deploys production-ready machine learning models using the industry-standard scikit-learn library.
Trains and deploys complex neural network architectures within distributed E2B sandbox environments for scalable machine learning workflows.
Explains machine learning model predictions and feature importance using SHAP values and comprehensive visualizations.
Accelerates data manipulation and ETL pipelines with the high-performance Polars DataFrame library.
Creates, edits, and analyzes Excel spreadsheets with professional formatting, automated formula recalculation, and integrated data visualization.
Builds and optimizes Retrieval-Augmented Generation (RAG) systems using advanced vector search, semantic chunking, and retrieval patterns.
Implements high-performance adaptive learning and memory distillation for self-improving AI agents using AgentDB.
Queries the Federal Reserve Economic Data (FRED) API to retrieve over 800,000 economic time series for financial research and macroeconomic analysis.
Streamlines the creation, fitting, and validation of Bayesian models using PyMC's modern probabilistic programming interface.
Designs, simulates, and executes quantum circuits using Google's Cirq framework for NISQ-era hardware and noise-aware algorithms.
Provides programmatic access to global statistical data for demographic, economic, and environmental research.
Automates the end-to-end scientific research pipeline from hypothesis generation and data analysis to publication-ready LaTeX manuscripts.
Automates protein testing and validation through a cloud-based laboratory platform for accelerated biotechnological research and sequence optimization.
Accesses AI-ready drug discovery datasets and benchmarks for therapeutic machine learning and pharmacological prediction.
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