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Found 40 Skills
Statistical models library for Python. Use when you need specific model classes (OLS, GLM, mixed models, ARIMA) with detailed diagnostics, residuals, and inference. Best for econometrics, time series, rigorous inference with coefficient tables. For guided statistical test selection with APA reporting use statistical-analysis.
Analyze datasets to extract insights, identify patterns, and generate reports. Use when exploring data, creating visualizations, or performing statistical analysis. Handles CSV, JSON, SQL queries, and Python pandas operations.
Evaluate scientific claims and evidence quality. Use for assessing experimental design validity, identifying biases and confounders, applying evidence grading frameworks (GRADE, Cochrane Risk of Bias), or teaching critical analysis. Best for understanding evidence quality, identifying flaws. For formal peer review writing use peer-review.
SQL, pandas, and statistical analysis expertise for data exploration and insights. Use when: analyzing data, writing SQL queries, using pandas, performing statistical analysis, or when user mentions data analysis, SQL, pandas, statistics, or needs help exploring datasets.
Pandas for time series analysis, OrcaFlex results processing, and marine engineering data workflows
Data analysis expert for statistics, visualization, pandas, and exploration
Best practices for SciPy scientific computing, optimization, signal processing, and statistical analysis in Python
Expert-level research methodology, academic writing, statistical analysis, and scientific investigation
Design and execute marketing A/B tests for landing pages, email campaigns, ad creatives, and pricing with proper test design and result analysis. Use this skill when the user needs to test marketing variations, improve conversion rates through experimentation, or decide between two campaign approaches — even if they say 'which version performs better', 'test this landing page', 'A/B test our email subject line', or 'should we change our CTA'.
SQL for data analysis with exploratory analysis, advanced aggregations, statistical functions, outlier detection, and business insights. 50+ real-world analytics queries.
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.
Data analysis, SQL queries, BigQuery operations, and data insights. Use for data analysis tasks and queries.