发现data science & ml类别的 Claude 技能。浏览 61 个技能,找到适合您 AI 工作流程的完美功能。
Creates, edits, and analyzes Excel spreadsheets with professional formatting, automated formula recalculation, and integrated data visualization.
Accelerates data manipulation and ETL pipelines with the high-performance Polars DataFrame library.
Explains machine learning model predictions and feature importance using SHAP values and comprehensive visualizations.
Trains and deploys complex neural network architectures within distributed E2B sandbox environments for scalable machine learning workflows.
Builds, evaluates, and deploys production-ready machine learning models using the industry-standard scikit-learn library.
Implements and trains advanced reinforcement learning algorithms to create autonomous agents that evolve through experience.
Develops and trains Graph Neural Networks (GNNs) for node classification, link prediction, and geometric deep learning tasks.
Generates interactive, publication-quality Python charts and dashboards for data exploration and presentation.
Performs automated exploratory data analysis and generates comprehensive reports for over 200 scientific file formats.
Generates publication-ready scientific figures and multi-panel layouts following strict journal specifications for Nature, Science, and Cell.
Implements standardized Agent-to-Agent (A2A) protocol executors with production-ready patterns for task management and agent coordination.
Performs advanced mathematical physics computations, symbolic algebra, and automated theorem proving using the Theory2 suite.
Analyzes CSV files automatically to generate comprehensive statistical summaries and context-aware visualizations using Python and pandas.
Performs comprehensive clinical trial design and statistical analysis in R, covering sample size calculation, randomization, and regulatory-compliant modeling.
Evaluates machine learning model performance using R's yardstick and tidymodels ecosystem for robust classification and regression analysis.
Performs fast, scalable nonlinear dimensionality reduction for high-dimensional data visualization, clustering, and feature engineering.
Optimizes machine learning models using comprehensive hyperparameter tuning patterns within the R Tidymodels ecosystem.
Simplifies complex bioinformatics workflows in R using Bioconductor for RNA-seq, microarray, and single-cell genomic analysis.
Implements end-to-end machine learning pipelines in R using the tidymodels ecosystem, from data splitting to model deployment.
Automates the calculation and interpretation of key financial ratios and metrics from diverse financial statement data sources.
Facilitates advanced Bayesian statistical modeling in R using Stan-based packages for comprehensive data analysis and inference.
Automates end-to-end scientific research workflows from data analysis and hypothesis generation to publication-ready LaTeX papers.
Access and benchmark hundreds of LLM models through a unified API to optimize for cost, performance, and response quality.
Provides expert strategies and domain knowledge for analyzing metabolic pathways, flux measurements, and biochemical mechanisms.
Provides specialized strategies and code patterns for genomics and transcriptomics data analysis, visualization, and biological interpretation.
Generates highly customizable, publication-quality static and interactive plots using Python's foundational visualization library.
Analyzes and validates protein structures, interprets AlphaFold predictions, and performs comparative molecular modeling.
Conducts high-performance computational fluid dynamics (CFD) simulations using Python-based pseudospectral methods and MPI parallelization.
Analyzes and visualizes complex network structures and graph data within Python environments.
Processes and analyzes massive tabular datasets exceeding available RAM using out-of-core DataFrames and lazy evaluation.
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