tensorflow-guide
TensorFlow best practices for tf.function, GPU memory, and deployment
Browse reusable Agent Skills, each with a clear purpose and practical guidance.
TensorFlow best practices for tf.function, GPU memory, and deployment
This skill should be used when the user asks to "implement a DiD regression", "write a causal inference pipeline", "set up an event study", "implement instrumental variables", "run a regression discontinuity design", "build a synthetic control model", "implement propensity score matching", "write parallel trends test", "implement Bacon decomposition", or needs code templates for causal inference methods in Python, R, or Stata. Based on Scott Cunningham's Causal Inference: The Mixtape.
7 code execution skills. Trigger: running code, interactive notebooks, Jupyter, Colab, sandboxed execution. Design: execution environment guides with setup instructions and best practices.
Spatial data: GeoDataFrames, spatial joins, CRS/projections, choropleth/interactive maps, spatial autocorrelation, PySAL. Use for geographic data, spatial files (Shapefile, GeoPackage, GeoParquet), or spatial stats. For charts without GIS use plotly.
Creates a Jupyter notebook with Jupytext pairing and registers it in _quarto.yml. Use when adding a new notebook.
Polars DataFrame library for high-performance data manipulation. Lazy/eager execution, expressions, I/O (CSV, Parquet, JSON), aggregations, joins, string/datetime ops, pandas interop. Use for Polars DataFrames or reading/writing Parquet files.
Use when writing Python code for DSGE models, HANK models, numerical economic computation, causal inference, or quantitative economic data analysis
R-to-Python translation for data analysis. Maps R packages (tidyverse, ggplot2, fixest, survey, sf, plm) to Python equivalents (polars, plotnine, pyfixest, svy, geopandas). Use when user has R background or requests R-equivalent code comments.