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Found 20 Skills
Use this skill whenever the user wants to work with survey data using the `survy` Python library. Triggers include: loading or reading survey CSV/Excel/JSON/SPSS files, handling multiselect (multi-choice) questions, computing frequency tables or crosstabs, exporting survey data to SPSS (.sav) or other formats, updating variable labels or value indices, transforming survey data between wide/compact formats, filtering respondents, replacing values, adding/dropping/sorting variables, or any task involving survy's API (read_csv, read_excel, read_json, read_polars, read_spss, crosstab, survey["Q1"], to_spss, to_csv, to_excel, to_json, etc.). Also trigger when the user says things like "analyze my survey", "process questionnaire data", "build a survey analysis script", or "help me with survy". Always read this skill before writing any survy code — it contains the correct API, patterns, and gotchas.
Local development environment management for Polar using Docker
Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.
Executive-grade data analysis with pandas/polars and McKinsey-quality visualizations. Use when analyzing data, building dashboards, creating investor presentations, or calculating SaaS metrics.
An analytical in-process SQL database management system. Designed for fast analytical queries (OLAP). Highly interoperable with Python's data ecosystem (Pandas, NumPy, Arrow, Polars). Supports querying files (CSV, Parquet, JSON) directly without an ingestion step. Use for complex SQL queries on Pandas/Polars data, querying large Parquet/CSV files directly, joining data from different sources, analytical pipelines, local datasets too big for Excel, intermediate data storage and feature engineering for ML.
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.
Best practices for developing tools, dashboards and interactive data apps with HoloViz Panel. Create reactive, component-based UIs with widgets, layouts, templates, and real-time updates. Use when developing interactive data exploration tools, dashboards, data apps, or any interactive Python web application. Supports file uploads, streaming data, multi-page apps, and integration with HoloViews, hvPlot, Pandas, Polars, DuckDB and the rest of the HoloViz and PyData ecosystems.
XGBoost gradient boosting library. Use for tabular ML.