Claude Skills by DAAF-Contribution-Community

Guide for creating DAAF agent definition files. Covers 12-section template, hook registration, skills-in-frontmatter, integration checklist. Use when adding or revising agents. For SKILL.md files, use skill-authoring instead.

Researcher-facing analytic triangulation ("creative verification") for stress-testing provisional findings and designing pre-specified challenges before an analysis runs. Turns a researcher's concern or focal claim into competing explanations, bounded discriminating tests, audited diagnostic artifacts, and evidence-calibrated interpretations in a hypothesis/evidence ledger — behavioral, output-level verification replacing line-by-line code review. Covers 12 challenge families organized by Sha...

In-situ smoke testing that verifies a LIVE DAAF deployment functions end-to-end in whatever provider route it is configured for. Auto-detects the active install route (anthropic-subscription, openrouter, chatgpt-subscription shim, openai-api shim) and runs tiered probes: Tier 0 free preflight (route/env coherence, hooks, statuslines, shim health); Tier 1 one live round-trip (~cents); Tier 2 a six-probe functional battery (dispatch, coding, web, skill loading, isolation-strip, nested-dispatch ...

Operational framework for the DAAF orchestrator. Defines engagement modes, confirmation protocol, subagent dispatch, context budget, and reference-loading. Loaded exclusively by the orchestrator — not for subagents or user questions.

Data science methodology and method-selection routing for quantitative research. Covers EDA, data validation, descriptive analysis, causal inference (IV, DiD, RD, synthetic control), clustering/PCA/UMAP, supervised ML, geospatial analysis, network analysis, and visualization design. Contains the canonical method-to-library routing tree, routed by execution language — Python: statsmodels (OLS/GLM/time series), pyfixest (FE/DiD), linearmodels (RE/GMM/SUR), svy (complex surveys), scikit-learn (c...

Interpretation guidance for Urban Institute Education Data Portal datasets. The Portal is a curation layer over federal data: lowercase variable names, integer-encoded categoricals, standardized missing codes (-1 missing, -2 not applicable, -3 suppressed). Covers year definitions, grade encoding (grade=-1 is Pre-K, not missing), suppression rates, ODC-By licensing, and cross-source join identifiers. Load before analyzing any Portal data. Routes to source-specific deep-dive skills for individu...

Mirror-first discovery for Urban Institute Education Data: maps a research question to the mirror dataset FILES that answer it — canonical path, entity grain, year coverage, key variables, and join keys — for CCD, IPEDS, CRDC, College Scorecard, SAIPE, EDFacts, FSA, MEPS, NHGIS, PSEO, NACUBO, NCCS, EADA, and Campus Safety. Discovery output is the mirror path (plus grain, years, variables, caveats), the currency planning and acquisition consume. A pure readable-reference skill for read-only di...

Downloads education datasets from configured mirror sources (parquet/CSV) with local Polars filtering, including versioned mirror vintages (revision-pinned for reproducibility). Use when writing fetch scripts, retrieving CCD, IPEDS, CRDC, SAIPE data, or pinning a fetch to a specific mirror vintage. Load after education-data-explorer — retrieval here, not discovery.

CSS — annual Clery Act crime/fire safety for Title IV institutions. Portal: hate crimes only (2005-2021); primary offenses, VAWA, arrests, fire safety need ope.ed.gov directly. Use for campus crime analysis. Identified by IPEDS unitid.

CCD — federal universe of all U.S. public K-12 schools (~100K) and districts (~18K). Enrollment, staffing, finance, directory data (1986-present). Use for public school analysis by grade/race/sex. Public only; excludes private and postsecondary.

CRDC — OCR civil rights collection for U.S. public schools; Portal families have topic-specific coverage from 2011 through 2022. Discipline, course access, harassment, restraint/seclusion by race/sex/disability/EL. Use for civil rights and equity analysis. Official evidence identifies 2013-14 as a universe collection; 2020-21 is COVID-impacted.

EADA — college athletics gender equity (~2,000+ institutions, 2002-2021). Participation, coaching, salaries, expenses, revenues, athletic aid by gender. Not Title IX compliance data. No sector column; join IPEDS on unitid for institution type.

EDFacts — K-12 outcomes: assessment proficiency, ACGR graduation rates, ESSA accountability at school/district level (2009-2020). Within-state trends and subgroup gaps. Complements CCD with outcome data. Cannot compare across states — use NAEP.

FSA — Title IV aid at institution level (~5,500 institutions, 1999-2021). Pell Grants, Direct/PLUS loans, campus-based aid, financial responsibility scores, 90/10 metrics. Use for aid distribution, loan volume, or for-profit analysis. By unitid.

IPEDS — primary federal postsecondary data (~6,500 institutions, 1979-present in the mirror; the IPEDS program dates from 1980): enrollment, completions, graduation rates, finance, aid, admissions, HR. For college/university analysis. Grad rates = first-time full-time; finance needs GASB/FASB care.

MEPS — Urban Institute modeled school-level poverty (% at 100% FPL) for public schools. MEPS 2.0 (released 2025-12-11) covers school years 2009-10 through 2022-23 and is planned for annual updates; Portal year 2022 returned rows while 2023 was empty on 2026-08-06 (re-probed). Use when FRPL is unreliable due to CEP and for consistent cross-state measurement.

NACUBO endowment data (~650 institutions, 2012-2022). Portal: 7 columns only (total endowment, per-FTE, YoY change). Use for endowment size/trends. Full investment/spending needs direct NACUBO access. For all-institution coverage use IPEDS finance.

NCCS — Form 990 data for private nonprofit colleges (Portal: IPEDS-matched, 1993-2016). Revenue, expenses, assets, endowment, governance beyond IPEDS. Use when IRS financial depth needed. Portal ends 2016; public institutions excluded (no Form 990).

NHGIS — census geography crosswalks via Portal: links schools (ncessch) and colleges (unitid) to census tracts, block groups, CBSAs, and regions (census-geography vintages 1990-2020; mirror file-year/linkage coverage 1980-2023). Portal provides geography linkage tables ONLY; browse census demographics in the public NHGIS Data Finder, then sign in with a free IPUMS NHGIS account to submit, manage, or download extracts. API extract requests also need an API key. Use for linking education or ins...

PSEO — Census data linking graduates to employment via LEHD wage records. Earnings percentiles at 1/5/10 years post-graduation by institution, degree, and CIP. Use for graduate earnings analysis. Coverage changes by release; Census V4.13.0 (2025Q4) lists 952 institutions and partners in 32 states plus D.C.

SAIPE — annual Census poverty estimates for school districts (Portal; county/state not in Portal). Use for district poverty, Title I context, or trends. ~18-month lag. No race/ethnicity disaggregation at district level — use ACS 5-year for that.

College Scorecard — post-enrollment outcomes linking aid records to IRS/Treasury earnings. Earnings, loan repayment, debt via six Portal sub-datasets. Use when tax-record-based earnings needed. Tracks only Title IV aid recipients, not all students.

County Presidential Returns 2000-2024 (MIT MEDSL). Vote shares, party trends, turnout by county_fips (joins census/education data). Requires HARVARD_DATAVERSE_API_KEY set via environment_settings.txt. Critical: naive mode='TOTAL' filtering silently drops ~1,000 counties in post-2020 data where states report by vote mode (absentee, election-day, provisional) instead of totals — use 3-pattern reconstruction (TOTAL-present rows kept, breakdown-only counties summed across modes, empty-string mode...

Fast high-dimensional fixed effects in R: feols/fepois/feglm/fenegbin with multi-way FE; IV estimation; DiD (TWFE, Sun-Abraham via sunab); clustered/ heteroskedasticity-robust SEs; etable/coefplot/iplot for reporting. Use when execution language is R. Python equivalent: pyfixest. For panel RE/between use plm; for GLM without FE use r-stats.

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).

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.

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 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).

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-grap...

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 (OSM...

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).

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 rese...

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.

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.

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).

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).

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).

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).

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.

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.

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.

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.

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

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).

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.

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.

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.

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.

Stata-to-R translation for data analysis. Maps Stata commands (reghdfe, xtreg, ivregress, margins, esttab, svy:) to R equivalents (fixest, plm, survey, marginaleffects, modelsummary). Use when user has Stata background or requests Stata-equivalent code comments in R pipelines.

Statistical modeling: OLS/WLS/GLS, GLM (logit, probit, Poisson), time series (ARIMA, VAR), mixed effects, diagnostics. Formula API. Use for regressions without fixed effects, GLMs, or time series. For FE/DiD use pyfixest; panel/IV use linearmodels. R equivalent: r-stats (use when execution language is R).