comparative-analysis
Systematic comparison of segments, cohorts, or time periods - ensure fair apples-to-apples comparisons, identify meaningful differences, explain WHY differences exist
Systematic comparison of segments, cohorts, or time periods - ensure fair apples-to-apples comparisons, identify meaningful differences, explain WHY differences exist
Create publication-quality scientific figures with matplotlib, seaborn, and plotly. Includes multi-panel layouts, error bars, significance markers, colorblind-safe palettes, and journal-specific export (PDF/EPS/TIFF). Use when creating figures for manuscripts, presentations, or any research visualization.
List all Danmarks Statistik tables currently stored in DuckDB with metadata. Use when user wants to know what data is available locally or explore stored tables.
根据用户描述智能选择最合适的图表类型并生成 Mermaid 代码。支持流程图、时序图、类图、ER图、甘特图、状态图等全部类型,配色鲜艳美观。
Browse Danmarks Statistik subject hierarchy to explore available data topics and categories. Use when user wants to discover what data is available or explore DST's organizational structure.
GraphDB可視化エージェント - RyuGraphデータベースの内容をMermaid/DOT/HTML形式で可視化。/visualize-graph [出力パス] で呼び出し。
Pragmatic qualitative analysis for interview data in sociology research. Guides you through systematic coding, interpretation, and synthesis with quality checkpoints. Supports theory-informed (Track A) or data-first (Track B) approaches.
Master high-performance rendering for large datasets with Datashader. Use this skill when working with datasets exceeding 100M+ points, optimizing visualization performance, or implementing efficient rendering strategies with rasterization and colormapping techniques.
Generate HTML research reports with embedded data, charts, and analysis. Use when creating final research deliverables. Supports single comprehensive reports or multiple focused reports. Handles styling, structure, and output to reports/ directory.
Generate phase portraits for 2D dynamical systems. Use when visualizing vector fields, nullclines, and trajectories.
Execute numerical calculations and mathematical computations using Julia. Use this skill for matrix operations, linear algebra, numerical integration, optimization, statistics, and scientific computing tasks.
Create beautiful data visualizations with mathematical elegance, color theory, and narrative design - the "Data is Beautiful" aesthetic.
Guide revenue analysis using ChartMogul reports. Use when discussing MRR, ARR, churn, retention, cohorts, or subscription metrics. Helps select the right report and interpret results.
Calculate and interpret SaaS growth metrics including MRR, ARR, churn rate, LTV, CAC, NRR, and conversion rates. Use when user mentions metrics, asks about business health, wants to calculate KPIs, or needs help interpreting growth numbers. Provides health checks against industry benchmarks.
Provides expert design guidance for creating truthful, clear, beautiful data visualizations. Focuses on **DESIGN DECISIONS ONLY**—chart selection, color strategy, visual encoding, and validation. Assumes data is accurate and prepared. Auto-activates when user mentions: data viz, dashboard, chart type, visualization, infographic
BI fundamentals with metric definition, KPI calculation, dimensional modeling, dashboard optimization, and data storytelling. 40+ metric examples and calculation patterns.
Use when analyzing sales performance, customer metrics, inventory health, or generating forecasts.
Generate comprehensive market research reports (50+ pages) in the style of top consulting firms (McKinsey, BCG, Gartner). Features professional LaTeX formatting, extensive visual generation with project-diagrams and generate-image, deep integration with research-lookup for data gathering, and multi-framework strategic analysis including Porter's Five Forces, PESTLE, SWOT, TAM/SAM/SOM, and BCG Matrix.
This skill should be used when creating charts and visualizations from backtest results. It provides patterns for equity curves, drawdown charts, return distributions, rolling metrics, and interactive dashboards using matplotlib, plotly, and seaborn.
Generic marketing campaign performance analysis patterns. Use when tracking ad spend, calculating ROI, optimizing budgets, or analyzing multi-channel performance. Framework for project-specific implementations.
This skill should be used when creating charts, graphs, dashboards, or data visualizations - covers chart type selection, D3.js patterns, Recharts usage, dashboard design principles, and data storytelling techniques for analytics-reporter and finance-tracker agents.