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。
Develops predictive player projection models using specialized feature engineering and sports-specific machine learning validation techniques.
Accesses and analyzes real-time SEC filings and financial statements with token-efficient data retrieval.
Fetches official, up-to-date documentation and code examples for the R Tidyverse ecosystem.
Formulates testable scientific hypotheses and experimental designs based on empirical observations and literature data.
Interprets, generates, and captions publication-quality scientific figures for quantitative academic research.
Implements fast non-linear dimensionality reduction for high-dimensional data visualization and clustering preprocessing.
Automates the transition from literature review to experimental design by analyzing academic papers and proposing novel research methods.
Optimizes high-performance data manipulation and ETL pipelines using the Polars DataFrame library.
Queries official R Shiny framework documentation to build, debug, and deploy interactive web applications.
Streamlines the implementation, training, and evaluation of production-ready reinforcement learning algorithms using a scikit-learn-like API.
Discovers, compares, and recommends R packages by fetching real-time data from CRAN and official Task Views.
Facilitates complex individual participant data (IPD) meta-analyses using tidy R workflows and advanced statistical modeling.
Automates professional spreadsheet creation, complex financial modeling, and data analysis with full support for formulas, formatting, and error-free recalculation.
Executes, monitors, and optimizes DBT data transformation pipelines with intelligent error handling and performance reporting.
Accesses real-time R package documentation, vignettes, and task views across CRAN, tidyverse, and Bioconductor ecosystems.
Performs advanced health economic evaluations including cost-effectiveness analysis, Markov modeling, and sensitivity analysis using R.
Generates publication-ready methods and results sections for clinical prediction and machine learning papers using TRIPOD+AI standards.
Standardizes AI/ML delivery with end-to-end guidance on experiment design, model validation, and governance for production-grade AI.
Enforces rigorous academic integrity and adherence to international reporting guidelines for quantitative research manuscripts.
Performs SQL data analysis, identifies trends, and generates comprehensive business intelligence reports directly within your workspace.
Implements comprehensive survival analysis workflows in R using tidy and traditional biostatistical frameworks.
Implements and simulates complex adaptive clinical trial designs using industry-standard R packages like adaptr, rpact, and RBesT.
Translates natural language into precise DBT semantic layer queries with automated filtering, visualization, and context-aware data exploration.
Standardizes Indirect Treatment Comparison (ITC) analyses in R using tidy modeling principles and reproducible workflow patterns.
Implements comprehensive machine learning pipelines in R using the tidymodels ecosystem, from data preprocessing to model deployment.
Provides foundational knowledge and best practices for developing, optimizing, and reviewing Stan 2.37 probabilistic models.
Simulates time-to-event clinical trial data and performs complex statistical analyses including weighted logrank and MaxCombo tests.
Performs comprehensive epidemiological analysis in R, covering study designs, causal inference, and measures of association.
Optimizes clinical trial designs through advanced sample size determination, event count tuning, and multi-objective tradeoff analysis.
Implements hierarchical and multilevel Bayesian models with optimized parameterizations for robust statistical inference.
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