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Found 21 Skills
Work in an existing vignette catalog: set it up, run or inspect its marimo notebooks, and answer data questions by reusing their @app.function helpers in a live kernel. Use whenever a repository has catalog.toml and the user asks to get started, run notebooks, explore the data, make an analysis or figure, or compose a new notebook. Do not use for generic notebooks or for creating a new catalog.
Create or adopt a repository as a vignette catalog for one dataset. Use when the user asks to start, scaffold, or convert a project into a catalog with catalog.toml and reusable marimo notebooks. Produces the smallest runnable structure and orientation notebook. Do not use for an existing catalog; use vignette-catalog-compose-notebook there.
Add Lens to a live notebook, explain a notebook with a guided Trail, or address a user's selection on a rendered output or configured DOM target. Use when asked to enable Lens, give a walkthrough, explain results in place, or act on selected points, regions, and notes. The address mode handles every open selection. Requires the live marimo notebook kernel, reached through marimo pair or the editor's code-mode sidebar.
Use for Jupyter, JupyterLab, marimo, and Google Colab workflows. Choose this skill when creating, converting, or improving reproducible notebooks for data exploration, analysis, documentation, or teaching.
Access PUDL table data plus table/column/source metadata in Jupyter or Marimo notebooks for debugging and visualization. Use when users ask what a table contains, how to read it, or how columns are defined.
Convert a Streamlit app to a marimo notebook
Check if a marimo notebook is compatible with WebAssembly (WASM) and report any issues.
Use this skill for data pipeline work — ingestion with dlt, transformations with sqlmesh, analytics with DuckDB/MotherDuck, DataFrames with polars, notebooks with marimo, and project management with uv.
Data analysis, visualization, and storytelling skill for financial and RevOps contexts. Use when: analyzing revenue data, building forecasts, cohort analysis, churn modeling, pipeline analytics, creating data-driven reports, building dashboards, cleaning messy data, sanity-checking analytical claims, exporting to Excel with formulas, or extracting data from PDFs. Features decision logging, bias-aware interpretation, and progressive disclosure (slide deck -> detailed report -> full notebook with all decisions documented).