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SkillsMP は DAAF-Contribution-Community/daaf から 56 件の skill を収集しています。skill を開くとソースと詳細を確認できます。
収集済み skill 56 件中 40 件を表示しています。
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.
原文の言語: 英語
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,…
原文の言語: 英語
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.
原文の言語: 英語
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.
原文の言語: 英語
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.…
原文の言語: 英語
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.
原文の言語: 英語
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.
原文の言語: 英語
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…
原文の言語: 英語
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…
原文の言語: 英語
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.
原文の言語: 英語
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).
原文の言語: 英語
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…
原文の言語: 英語
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…
原文の言語: 英語
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.
原文の言語: 英語
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.
原文の言語: 英語
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).…
原文の言語: 英語
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…
原文の言語: 英語
R data manipulation with dplyr, tidyr, readr, purrr, stringr, forcats, lubridate. Verb pipelines, reshaping, joins, string/date ops, parquet via arrow. Includes data.table for performance. Use when execution language is R. Python equivalent: polars.
原文の言語: 英語
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…
原文の言語: 英語
DAAF's protocol for retrieving web documents that an agent needs to READ (docs pages, articles, API references, issue threads). Retrieval runs through the framework CLI `bash /daaf/scripts/web_fetch.sh URL DEST_DIR`, which saves the raw response, a…
原文の言語: 英語
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…
原文の言語: 英語
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.
原文の言語: 英語
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…
原文の言語: 英語
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…
原文の言語: 英語
Privacy-preserving workflow for building synthetic datasets and data source skills when raw data must never enter the DAAF container. The user profiles their sensitive data locally with a disclosure-controlled script; only a summary profile report crosses the…
原文の言語: 英語
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/).…
原文の言語: 英語
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…
原文の言語: 英語
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.
原文の言語: 英語
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.
原文の言語: 英語
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…
原文の言語: 英語
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…
原文の言語: 英語
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.
原文の言語: 英語
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,…
原文の言語: 英語
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…
原文の言語: 英語
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.
原文の言語: 英語
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…
原文の言語: 英語
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,…
原文の言語: 英語
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…
原文の言語: 英語
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…
原文の言語: 英語
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.…
原文の言語: 英語