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Found 32 Skills
Expert-level research methodology, academic writing, statistical analysis, and scientific investigation
Implements comprehensive backtesting capabilities for Pine Script indicators and strategies. Use when adding performance metrics, trade analysis, equity curves, win rates, drawdown tracking, or statistical validation. Triggers on "backtest", "performance", "metrics", "win rate", "drawdown", or testing requests.
Design, plan, and analyze A/B tests with statistical rigor. Use when the user asks about A/B testing, split testing, experiment design, statistical significance, sample size calculation, test duration, multivariate testing, or conversion experiments. Trigger phrases include "A/B test", "split test", "experiment", "statistical significance", "sample size", "test duration", "which version wins", "conversion experiment", "hypothesis test", "variant testing".
Эксперт A/B тестирования. Используй для статистических тестов, экспериментов и ML-оптимизации.
Production-ready microscopy image analysis and quantitative imaging data skill for colony morphometry, cell counting, fluorescence quantification, and statistical analysis of imaging-derived measurements. Processes ImageJ/CellProfiler output (area, circularity, intensity, cell counts), performs Dunnett's test, Cohen's d effect size, power analysis, Shapiro-Wilk normality tests, two-way ANOVA, polynomial regression, natural spline regression with confidence intervals, and comparative morphometry. Supports CSV/TSV measurement tables, multi-channel fluorescence data, colony swarming assays, and neuron counting datasets. Use when analyzing microscopy measurement data, colony area/circularity, cell count statistics, swarming assays, co-culture ratio optimization, or answering questions about imaging-derived quantitative data.
Comprehensive CSV data analysis and visualization tool. Use this skill when analyzing CSV files, generating data summaries, creating visualizations from data, detecting outliers, finding correlations, assessing data quality, or creating data reports. Triggers on CSV analysis, data exploration, data visualization, data profiling, statistical analysis, or data quality assessment requests.
Perform statistical hypothesis testing, regression analysis, ANOVA, and t-tests with plain-English interpretations and visualizations.
Use when implementing data analysis pipelines, statistical tests, or bioinformatics workflows in code (Python/R), particularly for genomics, transcriptomics, proteomics, or other -omics data.
Data analysis expert for statistics, visualization, pandas, and exploration
Expert in statistical analysis, predictive modeling, machine learning, and data storytelling to drive business insights.
When the user wants to design, prioritize, or analyze growth experiments -- including A/B tests, hypothesis frameworks, ICE/RICE scoring, or growth sprints. Also use when the user says "A/B test," "experiment design," "growth sprint," "experiment prioritization," or "statistical significance." For analytics setup, see product-analytics. For growth modeling, see growth-modeling.
Analyze datasets to discover patterns, anomalies, and relationships. Use when exploring data files, generating statistical summaries, checking data quality, or creating visualizations. Supports CSV, Excel, JSON, Parquet, and more.