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Found 683 Skills
Best practices for creating comprehensive Jupyter notebook data analyses with statistical rigor, outlier handling, and publication-quality visualizations
This skill should be used whenever domain modeling is taking place. It provides specialized guidance for type-driven and data-driven design based on Rich Hickey and Scott Wlaschin's principles. The skill helps contextualize current modeling within the existing domain model, identifies inconsistencies, builds ubiquitous language, and creates visualizations (Mermaid, Graphviz/DOT, ASCII diagrams) to communicate domain concepts clearly. Use this skill when designing types, modeling business domains, refactoring domain logic, or ensuring domain consistency across a codebase.
Builds dashboards, reports, and data-driven interfaces requiring charts, graphs, or visual analytics. Provides systematic framework for selecting appropriate visualizations based on data characteristics and analytical purpose. Includes 24+ visualization types organized by purpose (trends, comparisons, distributions, relationships, flows, hierarchies, geospatial), accessibility patterns (WCAG 2.1 AA compliance), colorblind-safe palettes, and performance optimization strategies. Use when creating visualizations, choosing chart types, displaying data graphically, or designing data interfaces.
Transform CSV/Excel data into narrative reports with auto-generated insights, visualizations, and PDF export. Auto-detects patterns and creates plain-English summaries.
Dynamic orchestration engine that plans multi-step agent work as DAGs with Mermaid visualization.
Analyze CSV files, generate summary statistics, and create visualizations using Python and pandas. Use when the user uploads, attaches, or references a CSV file, asks to summarize or analyze tabular data, requests insights from CSV data, or wants to understand data structure and quality.
This skill should be used when the user needs to visualize BAM alignment files in IGV (Integrative Genomics Viewer). Triggers include requests to generate IGV screenshots, visualize genomic regions with multiple BAM tracks, or create batch visualizations for WGS analysis results.
Data visualization for charts and graphs. Use when user needs "画图/图表/可视化". Creates static PNG or interactive HTML charts from data.
Use when asked to create publication-ready scientific figures, charts for research papers, or academic visualizations.
Create publication-quality plots and visualizations using matplotlib and seaborn. Works with ANY LLM provider (GPT, Gemini, Claude, etc.).
Create and manage Kibana Dashboards and Lens visualizations. Use when you need to define dashboards and visualizations declaratively, version control them, or automate their deployment.
Create Vega and Vega-Lite visualizations with ES|QL data sources in Kibana. Use when building custom charts, dashboards, or programmatic panel layouts beyond standard Lens charts.