YouTube Data API v3 analytics toolkit. Analyze YouTube channels, videos, and search results. Use when the user asks to: check YouTube channel stats, analyze video performance, compare channels, search for videos, get subscriber counts, view engagement metrics, find trending videos, get channel uploads, or analyze YouTube competition. Requires a YouTube Data API v3 key from Google Cloud Console.
Generate professional tearsheets with custom SVG visualizations using the QuantStats library. Creates performance reports with MAE analysis, leverage recommendations, and trade lists. Use when analyzing strategy performance or generating visual reports.
Auto-generates comparison tables for concepts, drugs, or study results in Markdown format.
Generate standardized figure legends for scientific charts and graphs. Trigger when user uploads/requesting legend for research figures, academic papers, or data charts. Supports bar charts, line graphs, scatter plots, box plots, heatmaps, and microscopy images. This tool generates text legends only, not visualizations.
Beautify meta-analysis forest plots with customizable odds ratio points and confidence intervals
Summarize core safety information from Investigator's Brochures for clinical researchers
Show journal impact factor and quartile trends over 5 years.
Generates Mermaid flowchart code and visual diagrams for pathophysiological.
Use when creating forest plots for meta-analyses, visualizing effect sizes across studies, or generating publication-ready meta-analysis figures. Produces high-quality forest plots with confidence intervals, heterogeneity metrics, and subgroup analyses.
Analyze data with `metagenomic-krona-chart` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.
Basic sample size calculator for clinical research planning with common statistical scenarios
Analyze data with `smiles-de-salter` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.
Recommends appropriate statistical methods (T-test vs ANOVA, etc.) based.
Generates Kaplan-Meier survival curves, calculates survival statistics (log-rank test, median survival time), and estimates hazard ratios for clinical and biological survival data analysis. Triggered when user requests survival analysis, Kaplan-Meier plots, time-to-event analysis, or asks about survival statistics in biomedical contexts.
Analyze data with `survival-curve-risk-table` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.
Automated generation of baseline characteristics tables (Table 1) for clinical research papers.
Generates complete two-sample Mendelian randomization (MR) research designs from a user-provided research direction. Use when users want to design, plan, or build a study using two-sample MR to test causal relationships. Triggers: "design a two-sample MR study", "build a publishable MR paper", "test whether this biomarker causally affects this disease", "generate Lite/Standard/Advanced MR plans", "screen multiple exposures with MR", "bidirectional MR design", "causal inference using GWAS summary statistics", or "I want to study X and Y using MR". Always outputs four workload configurations (Lite / Standard / Advanced / Publication+) with a recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version, and publication upgrade path.
Generates detailed text descriptions of medical images and charts for.
World-class senior data scientist skill specialising in statistical modeling, experiment design, causal inference, and predictive analytics. Covers A/B testing (sample sizing, two-proportion z-tests, Bonferroni correction), difference-in-differences, feature engineering pipelines (Scikit-learn, XGBoost), cross-validated model evaluation (AUC-ROC, AUC-PR, SHAP), and MLflow experiment tracking — using Python (NumPy, Pandas, Scikit-learn), R, and SQL. Use when designing or analysing controlled experiments, building and evaluating classification or regression models, performing causal analysis on observational data, engineering features for structured tabular datasets, or translating statistical findings into data-driven business decisions.
