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Found 351 Skills
Fetch and analyze Readwise reading activity for any date range. Use when user asks about articles saved, highlights created, or most-highlighted content for today, yesterday, last week, last month, or custom date ranges (e.g., "show my Readwise activity from Jan 1-7"). Requires READWISE_API_TOKEN env var. Connects to Readwise Highlights API (v2) and Reader API (v3).
Create interactive chart visualizations (bar, line, pie) from data.
Use when asked to create publication-ready scientific figures, charts for research papers, or academic visualizations.
Build interactive data applications and dashboards with pure Python - no frontend experience required
Exploratory Data Analysis (EDA): profiling, visualization, correlation analysis, and data quality checks. Use when understanding dataset structure, distributions, relationships, or preparing for feature engineering and modeling.
Use this for exploratory data analysis (EDA), generating visualizations, finding trends, and deriving insights from datasets using Python (Pandas/Seaborn/Plotly) or SQL.
Perform cohort analysis on user engagement data — retention curves, feature adoption trends, and segment-level insights. Use when analyzing user retention by cohort, studying feature adoption over time, investigating churn patterns, or identifying engagement trends.
Help create paper-quality plots and figures with matplotlib or seaborn. Use when the user asks for plots, figures, or visualizations.
A user-empowering data visualization Vue 3 components library for eloquent data storytelling. ALWAYS use when writing code importing "vue-data-ui". Consult for debugging, best practices, or modifying vue-data-ui, vue data ui.
High-performance data analysis using Polars - load, transform, aggregate, visualize and export tabular data. Use for CSV/JSON/Parquet processing, statistical analysis, time series, and creating charts.
Create Mermaid diagrams and convert them to images. Use when needing to visualize flows, architecture, or data structures.
Assemble multi-panel scientific figures with panel labels (A, B, C) at publication quality (300 DPI) using R. Use when combining individual plots into journal-ready figures.