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Found 351 Skills
Install and use chartli to render terminal charts from numeric text files or stdin.
Use this skill any time the user wants to analyze data, create charts, or build data visualizations. This includes: sales analysis, financial modeling, cohort analysis, funnel analysis, A/B test results, KPI tracking, data reports, revenue breakdowns, user retention analysis, conversion rate analysis, CSV summarization, and dashboard creation. Also trigger when: user says 分析这组数据, 做个图表, 数据可视化, 销售分析, 漏斗分析, 留存分析, 做个数据报表. If data needs to be analyzed or visualized, use this skill.
Generate publication-ready scientific figures in Python/matplotlib with a consistent figures4papers house style. Use when creating or refining academic bar/trend/heatmap/scatter/multi-panel figures, enforcing visual consistency, or exporting paper-ready PNG/PDF/SVG outputs.
Implement and customize Syncfusion Angular Accumulation Charts (Pie, Doughnut, Pyramid, Funnel) with data binding, labels, legends, and tooltips. Use this when creating accumulation charts, configuring chart types, customizing data visualization, adding annotations, or handling interactive chart events in Angular applications.
Apache Superset integration. Manage data, records, and automate workflows. Use when the user wants to interact with Apache Superset data.
Design effective data dashboards with proper KPI hierarchy, chart type selection, and interactive features. Use this skill when the user needs to create a dashboard, choose the right visualizations, organize metrics for different audiences, or evaluate dashboard tools — even if they say 'build a dashboard', 'our reports are confusing', 'which chart should I use', or 'executives can't find the metrics they need'.
Plecto integration. Manage Organizations, Persons, Deals, Pipelines, Activities, Notes and more. Use when the user wants to interact with Plecto data.
Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that don't fit in memory.
This skill should be used when working with CSV files to create interactive data visualizations, generate statistical plots, analyze data distributions, create dashboards, or perform automatic data profiling. It provides comprehensive tools for exploratory data analysis using Plotly for interactive visualizations.
Data analysis best practices with pandas, numpy, matplotlib, seaborn, and Jupyter notebooks.
Create simple, responsive charts quickly with Chart.js
Build data visualization and analytics dashboards. Use when creating charts, KPI displays, metrics dashboards, or data visualization components. Triggers on analytics, dashboard, charts, metrics, KPI, data visualization, Recharts.