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. R equivalent: sf-terra (use when execution language is R).
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R visualization with ggplot2 grammar of graphics. Geoms, aesthetics, scales, facets, coords, themes. Extensions: patchwork, ggrepel, ggridges, ggdist. Use when execution language is R. Python equivalent: plotnine.
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Python table formatting with great-tables: publication-quality display tables from Polars/pandas DataFrames. GT() grammar-of-tables object, fmt_*() number formatting, tab_*() structure (header, spanners, stub, source notes), tab_style() and data_color() styling, cols_*() column operations, and HTML/LaTeX export via as_raw_html()/write_raw_html()/as_latex(). Use when execution language is Python and the task involves formatted data tables, summary tables, or descriptive-stat tables for reports. R equivalent: gt (use when execution language is R). For regression/model tables in Python, use the estimator's own output (pyfixest etable(), statsmodels summary()) — great-tables has no modelsummary equivalent. For figures/charts use plotnine or plotly, not this skill.
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R table formatting with gt, kableExtra, modelsummary. Publication-quality tables: gt() grammar-of-tables, fmt_*() formatting, tab_*() structure, gtsave() export. Use when execution language is R. Python equivalent: great-tables (use when execution language is Python).
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R network analysis with igraph fronted by tidygraph + ggraph: graph construction from edge-list tibbles (tidyverse round-trip), centrality (degree, betweenness, closeness, eigenvector, PageRank), community detection (Leiden, Louvain, walktrap) with seed discipline, shortest paths, components, bipartite construction + projection, and static grammar-of-graphics figures via ggraph with seeded layouts. Use for relational/graph data. For road-network routing use sf-terra (sfnetworks); for non-graph clustering use tidymodels. Use when execution language is R. Python equivalent: igraph.
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Network/graph analysis with python-igraph: graph construction from edge-list DataFrames (Polars round-trip), centrality (degree, betweenness, closeness, eigenvector, PageRank), community detection (Leiden, Louvain, walktrap) with seed discipline, paths/components, bipartite construction and projection, static visualization (matplotlib backend). Use for relational data — collaboration, friendship, organizational, or co-occurrence networks. For geographic road-network routing use geopandas (OSMnx); for non-graph clustering use scikit-learn. R equivalent: igraph-r (use when execution language is R).
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Panel data, IV/GMM, system regression. PanelOLS (FE/RE), BetweenOLS, Fama-MacBeth, IV2SLS/LIML/GMM, SUR, 3SLS, Driscoll-Kraay SEs. Use for RE/between, system estimation, or GMM. Complements pyfixest (FE + DiD) and statsmodels (GLM + time series). R equivalent: plm (use when execution language is R).
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Reactive Python notebook system with cell reactivity, UI elements, SQL cells, plotting, and app deployment. DAAF's standard notebook format — stored as Git-friendly .py files, not .ipynb. For DAAF pipelines, Stage 9 literally archives existing executed scripts in bounded comment/pass cells paired with real execution logs; optional display-only cells may preview existing Parquet data or show already-created figures, but may not analyze data or generate figures. Use when assembling Stage 9 research notebooks, building standalone interactive data apps, or converting Jupyter notebooks to marimo format. R equivalent: quarto — when execution language is R, use quarto instead.
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R panel data models with plm: within (FE), random (RE), between, first difference, Fama-MacBeth. Hausman test, panel unit root tests. Includes estimatr for robust estimation and lme4 for mixed effects. Use when execution language is R. Python equivalent: linearmodels. For high- dimensional FE use fixest; for cross-sectional OLS/GLM use r-stats.
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Interactive R visualization with plotly: plot_ly() for scatter, line, bar, heatmap, 3D charts; ggplotly() to convert ggplot2 objects; layout() for customization; htmlwidgets::saveWidget() for export. Use when execution language is R and interactivity needed. Python equivalent: plotly. For static figures use ggplot2.
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Plotly interactive visualization. Express and Graph Objects: scatter, line, bar, heatmap, 3D, geographic charts; subplots; styling; export. Use when interactivity (hover/zoom) is needed. For static figures use plotnine; for GIS use geopandas. R equivalent: plotly-r (use when execution language is R).
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plotnine static visualization (ggplot2 syntax for Python). Geoms, aesthetics, scales, coordinates, facets, themes. Use for static publication-quality figures with grammar-of-graphics syntax. For interactive charts use plotly; for maps use geopandas. R equivalent: ggplot2 (use when execution language is R).
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High-performance data manipulation with lazy/eager execution, expressions, I/O (CSV, Parquet, JSON), aggregations, joins, string/datetime ops, and pandas interop. Covers performance optimization patterns and common anti-patterns. DAAF's default DataFrame library — all pipeline code uses Polars, not pandas. Use for any DataFrame operation, reading/writing Parquet files, or migrating existing pandas code to Polars. R equivalent: tidyverse (use when execution language is R).
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Fast high-dimensional fixed effects: OLS, Poisson, IV with multi-way FE; DiD (TWFE, did2s, Sun-Abraham); clustered SEs; etable/coefplot/iplot. Use for FE regressions or DiD. For panel RE/between use linearmodels; for GLM without FE use statsmodels. R equivalent: fixest (use when execution language is R).
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Python-to-R translation for data analysis. Maps Python (polars, plotnine, pyfixest, statsmodels, svy, geopandas) to R (tidyverse, ggplot2, fixest, survey, sf). Use when user has Python background or requests Python-equivalent code comments in R pipelines.
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Quarto document system for R: .qmd format with knitr engine, YAML frontmatter, code chunks with execution options, rendering to HTML/PDF. DAAF's R notebook format — Stage 9 literally archives executed R scripts in globally and per-chunk non-evaluating chunks paired with real execution logs. Optional display-only content may preview existing Parquet data or show already-created figures, but may not analyze data or generate figures. Use when execution language is R. Python equivalent: marimo.
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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.
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R statistical modeling with base stats, sandwich, lmtest, car, broom. OLS/WLS/ GLS via lm(), GLMs via glm() (logit, probit, Poisson, negative binomial), robust/clustered SEs via sandwich+lmtest, diagnostics via car, tidy output via broom. Use when execution language is R. Python equivalent: statsmodels. For FE regressions use fixest; for panel RE/between use plm; for complex surveys use survey-r.
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Translating technical findings for non-technical audiences. Narrative frameworks (Pyramid Principle, SCQA), plain-language translation, executive summaries, policy briefs, causal language. Use when presenting to stakeholders or reviewing deliverables
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Machine learning: clustering, PCA/t-SNE/UMAP, classification, prediction regression (Ridge/Lasso/ensemble), cross-validation, Pipelines. For unsupervised analysis, classification, or prediction. For econometric regression use pyfixest/statsmodels. R equivalent: tidymodels (use when execution language is R).
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R spatial data: sf vectors, terra rasters, spdep/spatialreg spatial stats, leaflet interactive maps, ggplot2+geom_sf() choropleths. CRS, spatial joins, geometry ops. Use when execution language is R. Python equivalent: geopandas.
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Standards for Bash and PowerShell scripts in DAAF: preambles, quoting, error handling, cleanup, testing. Use when writing or reviewing .sh/.ps1 files (hooks, lifecycle, utilities) and the host double-click launcher shims (.bat/.command under scripts/host/). Not runtime safety — that is bash-safety.sh hook.
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Guide for creating and auditing DAAF skills (SKILL.md). Covers frontmatter, metadata vocabulary, progressive disclosure, decision trees, reference files. Use when creating, reviewing, or debugging skill loading. For agent files, use agent-authoring.
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Stata-to-Python translation for data analysis. Maps Stata commands (reghdfe, xtreg, ivregress, margins, esttab, svy:) to Python (polars, pyfixest, statsmodels, svy). Use when user has Stata background or requests Stata-equivalent code comments in Python pipelines.
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