Structured hypothesis formulation from observations. Use when you have experimental observations or data and need to formulate testable hypotheses with predictions, propose mechanisms, and design experiments to test them. Follows scientific method framework. For open-ended ideation use scientific-brainstorming; for automated LLM-driven hypothesis testing on datasets use hypogenic.
Statistical visualization library built on matplotlib with native pandas DataFrame support. Automatic aggregation, confidence intervals, and grouping for distribution plots (histplot, kdeplot), categorical comparisons (boxplot, violinplot, stripplot), relational plots (scatterplot, lineplot), regression plots (regplot, lmplot), matrix plots (heatmap, clustermap), and multi-variable grids (pairplot, jointplot, FacetGrid). Use seaborn for statistical summaries with minimal code; use matplotlib for fine-grained figure control; use plotly for interactive HTML output.
Statistical visualization built on matplotlib with pandas integration. Distribution plots (histplot, kdeplot, violinplot, boxplot), relational plots (scatterplot, lineplot), categorical comparisons, regression, correlation heatmaps. Automatic aggregation and CI. For interactive plots use plotly; for low-level control use matplotlib.
Graph and network analysis toolkit: create, manipulate, and analyze complex networks. Four graph types (directed, undirected, multi-edge), centrality measures, shortest paths, community detection, graph generators, I/O (GraphML, GML, edge list, pandas, NumPy), visualization with matplotlib. For large-scale graphs (100K+ nodes) use igraph or graph-tool; for graph neural networks use PyG.
NHGRI-EBI GWAS Catalog REST API for SNP-trait associations from published genome-wide association studies. Query studies, associations, variants, traits, genes, and summary statistics. Build polygenic risk score candidates, analyze variant pleiotropy, download summary statistics for Manhattan plots. No authentication required.
Aggregates QC outputs from 150+ bioinformatics tools into a single interactive HTML report. Scans directories for FastQC, samtools, STAR, HISAT2, Trim Galore, featureCounts, Kallisto, Salmon, Picard, and GATK logs; merges statistics across samples with interactive plots. Essential for NGS pipeline QC review. Use FastQC directly instead for single-sample initial assessment; MultiQC is for multi-sample pipeline-wide reporting.
MATLAB/GNU Octave numerical computing for matrix operations, linear algebra, differential equations, signal processing, optimization, statistics, and scientific visualization. Code examples in MATLAB syntax (runs on both MATLAB and Octave). For Python-based scientific computing use numpy/scipy; for statistical modeling use statsmodels.
Symbolic mathematics in Python: exact algebra, calculus (derivatives, integrals, limits), equation solving, symbolic matrices, differential equations, code generation (lambdify, C/Fortran). Use when exact symbolic results are needed, not numerical approximations. For numerical computing use numpy/scipy; for statistical modeling use statsmodels.
Conduct exploratory data analysis and statistical testing with test selection guidance. Use when exploring datasets, selecting statistical tests, performing power analysis, or preparing results for publication.
A simple calculator that can add, subtract, multiply, and divide numbers. Use when the user needs to perform basic arithmetic operations.
Analyze CSV/JSON data with statistics, filtering, and aggregation. Powered by pandas and numpy.
Evaluate alternative scenarios by simulating interventions on past decisions or hypothetical futures. Use when assessing decisions in hindsight, planning scenarios, or comparing paths not taken. Produces comparative analysis with probability-weighted outcomes.
Professional data visualization creation using D3.js with support for interactive charts, custom visualizations, animations, and responsive design. Use for: (1) Creating custom interactive charts, (2) Building dashboards, (3) Network/graph visualizations, (4) Geographic data mapping, (5) Time series analysis, (6) Real-time data visualization, (7) Complex multi-dimensional data displays
Creates and edits Excel spreadsheets with formulas, formatting, and financial modeling standards. Use when working with .xlsx files, financial models, data analysis, or formula-heavy spreadsheets. Covers formula recalculation, color coding standards, and common pitfalls.
Create interactive Desmos graphs in Obsidian using desmos-graph code blocks. Use when visualizing functions, parametric curves, inequalities, or mathematical relationships with customizable styling and settings.
Automatically generate daily construction reports from field data, worker inputs, weather, and progress photos. Creates professional PDF reports.
Generate professional cost estimation reports from CWICR calculations. HTML, PDF, Excel outputs with charts and breakdowns.
Extract quantities from IFC/Revit models for quantity takeoff. Uses DDC converters to get element counts, areas, volumes, lengths with grouping and reporting.
Calculate construction labor rates with overhead, benefits, and productivity factors. Regional rate databases and crew composition.
Generate detailed token usage report with threshold analysis and recommendations
Automatically generate PDF reports from construction data. Create formatted project reports with charts and tables.
Create visualizations for construction data. Generate charts, graphs, heatmaps, and interactive dashboards using Matplotlib, Seaborn, and Plotly for project analysis and reporting.
