daaf
Rigorous, reproducible data research pipelines with human oversight
Instructs the agent to act as a rigorous research orchestrator: enforce file-first script execution, inline audit-trail comments, parquet-only data storage, immutable script versioning, and evidence-graded reporting across all skills in this framework.
All Skills in This Repository (25)
Pure Emerald Level Indicators
DAAF-Contribution-Communityeducation-data-source-crdc
Document CRDC data elements, methodology, and legal context for educational equity analysis.
DAAF-Contribution-Communityeducation-data-query
Downloads educational datasets from configured mirror sources and filters them locally with Polars.
DAAF-Contribution-Communityeducation-data-source-nhgis
Access US Census Bureau data via NHGIS for education research.
DAAF-Contribution-Communityeducation-data-context
Explain data provenance, missing value codes, and variable definitions for Urban Institute Education Data Portal datasets.
DAAF-Contribution-Communityeducation-data-source-nccs
Access NCCS Form 990 data for private nonprofit higher education institutions.
DAAF-Contribution-Communityeducation-data-source-saipe
Retrieve U.S. Census Bureau SAIPE poverty estimates for school districts, counties, and states.
DAAF-Contribution-Communityelection-data-source-countypres
Fetch county-level U.S. presidential election returns from 2000-2024 via Harvard Dataverse.
DAAF-Contribution-Communityeducation-data-source-campus-safety
Retrieve and analyze U.S. Department of Education Campus Safety and Security data.
DAAF-Contribution-Communityeducation-data-source-fsa
Access Federal Student Aid datasets on grants, loans, and 90/10 revenue.
DAAF-Contribution-Communityplotly
Generate interactive Plotly charts from data with export options.
DAAF-Contribution-Communityeducation-data-source-scorecard
Analyze post-college earnings, debt, and loan repayment rates from College Scorecard data.
DAAF-Contribution-Communitydata-scientist
Enforce rigorous Python data analysis with validation, documentation, and verification.
Frequently Asked Questions
FAQPage SchemaHow to install DAAF?▼
Run `npx skills add DAAF-Contribution-Community/daaf --all -g -y` in your terminal to install all skills in this framework globally.
What is DAAF used for?▼
DAAF structures AI-assisted data analysis into auditable, reproducible research pipelines with human checkpoints at every key decision. It is built for researchers who need rigorous, verifiable results rather than quick unverified answers.
Does DAAF work with both Python and R?▼
Yes. DAAF supports Python (polars, pyfixest, plotnine, geopandas) and R (tidyverse, fixest, ggplot2, sf) execution, and includes cross-language translation skills for users coming from Stata, R, or Python backgrounds.
Can DAAF handle sensitive or private data?▼
Yes. Its synthetic-data workflow lets you profile sensitive data locally on your own machine and share only a disclosure-controlled summary, so raw data never leaves your environment.
What data sources does DAAF cover?▼
It ships with deep skills for major U.S. education datasets (CCD, IPEDS, CRDC, Scorecard, SAIPE, FSA, NCCS, NACUBO, MEPS, NHGIS) and MIT county-level presidential election returns, including coded values, caveats, and join keys.
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