analysis▌

Statistical methods for descriptive analysis, trend detection, outlier identification, and hypothesis testing with practical business interpretation. \n \n Covers descriptive statistics (mean, median, percentiles, spread measures), trend analysis with moving averages and period-over-period comparisons, and simple forecasting methods for business analysts \n Includes three outlier detection approaches (z-score, IQR, percentile methods) with guidance on investigating and handling anomalies rather

Comprehensive sentiment analysis report for individual stocks with bull and bear breakdowns. \n \n Aggregates sentiment scores and viewpoint counts across multiple bloggers for a single ticker \n Retrieves detailed arguments from both bullish and bearish perspectives with direct quotes and attribution \n Identifies blogger positions and conviction levels (heavy/light holdings) to gauge skin-in-the-game confidence \n Compiles risk factors and presents findings in a structured report format withou

Dataset exploration, cleaning, statistical analysis, and visualization in Python or SQL. \n \n Supports CSV, JSON, and SQL data sources with pandas DataFrames and direct database queries \n Covers the full analysis pipeline: data loading, missing value handling, outlier detection, grouping, correlation analysis, and pivot tables \n Includes visualization templates for histograms, boxplots, heatmaps, and time series using matplotlib and seaborn \n Generates structured markdown reports with datase

STRIDE Analysis Patterns \n Systematic threat identification using the STRIDE methodology. \n Use this skill when \n \n Starting new threat modeling sessions \n Analyzing existing system architecture \n Reviewing security design decisions \n Creating threat documentation \n Training teams on threat identification \n Compliance and audit preparation \n \n Do not use this skill when \n \n The task is unrelated to stride analysis patterns \n You need a different domain or tool outside this scope \n

Production-ready VCF processing and variant annotation skill combining local bioinformatics computation with ToolUniverse database integration. Designed to answer bioinformatics analysis questions about VCF data, mutation classification, variant filtering, and clinical annotation.

Comprehensive protein interaction network analysis using ToolUniverse tools. Analyzes protein networks through a 4-phase workflow: identifier mapping, network retrieval, enrichment analysis, and optional structural data.

Cohort analysis tracks groups of users with shared characteristics over time, revealing patterns in retention, engagement, and lifetime value.

Correlation analysis measures the strength and direction of relationships between variables, helping identify which features are related and detect multicollinearity.

Comprehensive skill for analyzing T-cell receptor (TCR) and B-cell receptor (BCR) repertoire sequencing data to characterize adaptive immune responses, clonal expansion, and antigen specificity.

Survival analysis studies time until an event occurs, handling censored data where events haven't happened for some subjects, enabling prediction of lifetimes and risk assessment.