data science & ml Claude 스킬을 발견하세요. 61개의 스킬을 탐색하고 AI 워크플로우에 완벽한 기능을 찾아보세요.
Implements multi-objective and single-objective optimization algorithms to solve complex engineering and mathematical problems.
Manages large N-dimensional arrays with chunking and compression for high-performance scientific computing and cloud storage.
Builds reactive Python notebooks, interactive dashboards, and data-driven applications using the marimo framework.
Facilitates creative research ideation and exploratory scientific problem-solving through structured conversational partnership.
Builds and optimizes reactive Python notebooks using marimo for interactive data analysis, dashboards, and machine learning workflows.
Enables professional-grade spreadsheet creation, financial modeling, and data analysis with automated formula verification and industry-standard formatting.
Query and analyze over 240 million scholarly works using the OpenAlex database for literature reviews and bibliometric studies.
Builds process-based discrete-event simulations in Python for modeling complex systems with shared resources.
Processes and analyzes massive tabular datasets exceeding available RAM using out-of-core DataFrames and lazy evaluation.
Analyzes and visualizes complex network structures and graph data within Python environments.
Conducts high-performance computational fluid dynamics (CFD) simulations using Python-based pseudospectral methods and MPI parallelization.
Generates structured, data-driven scientific research reports by analyzing repository data and verifying metrics against technical standards.
Analyzes and validates protein structures, interprets AlphaFold predictions, and performs comparative molecular modeling.
Generates highly customizable, publication-quality static and interactive plots using Python's foundational visualization library.
Provides specialized strategies and code patterns for genomics and transcriptomics data analysis, visualization, and biological interpretation.
Provides expert strategies and domain knowledge for analyzing metabolic pathways, flux measurements, and biochemical mechanisms.
Access and benchmark hundreds of LLM models through a unified API to optimize for cost, performance, and response quality.
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.
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