pipeline
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest +…
Discovers education data from Urban Institute Portal: endpoints, variables, year coverage, join keys (CCD, IPEDS, CRDC, Scorecard, SAIPE). Use to map questions to data. Load before education-data-query — discovery here, download there.
$ npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill education-data-explorer --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/education-data-explorerContext preview
The summary Claude sees to decide when to auto-load this skill.
Discovers education data from Urban Institute Portal: endpoints, variables, year coverage, join keys (CCD, IPEDS, CRDC, Scorecard, SAIPE). Use to map questions to data. Load before education-data-query — discovery here, download there.
name: education-data-explorer description: >- Discovers education data from Urban Institute Portal: endpoints, variables, year coverage, join keys (CCD, IPEDS, CRDC, Scorecard, SAIPE). Use to map questions to data. Load before education-data-query — discovery here, download there. metadata: audience: research-planner domain: data-access
Discovers available education data from the Urban Institute Education Data Portal: endpoints, variables, year coverage, and join keys for schools, districts, and colleges (CCD, IPEDS, CRDC, Scorecard, SAIPE, and more). Use during discovery and scoping phases when identifying what data exists, mapping research questions to endpoints, or resolving variable name discrepancies between documentation and actual field names. Load before education-data-query — this skill covers discovery; education-data-query handles the download.
Discover available education data from the Urban Institute Education Data Portal for research planning and query design.
> **Skill Provenance Note:** Each `*-data-source-*` skill includes > `provenance.skill_last_updated` in its frontmatter. When exploring data > sources during Stage 2, note the provenance dates of any skills you > reference — if more than a few months old, flag this in your findings > so the orchestrator can consider re-verifying with data-ingest.
> **Note:** This workflow uses mirror-based file downloads, not paginated API calls. See `education-data-query` skill for fetch patterns and `datasets-reference.md` for file paths.
| File | Purpose | When to Read | |------|---------|--------------| | `schools-endpoints.md` | All school-level endpoints and variables | Researching K-12 schools | | `districts-endpoints.md` | All district-level endpoints and variables | Researching school districts | | `colleges-endpoints.md` | All college-level endpoints and variables | Researching higher education | | `variable-codes.md` | Code values for states, grades, race, etc. | Interpreting or filtering data | | `metadata-api.md` | Programmatic endpoint/variable discovery | Dynamic exploration |
What entity am I researching?
├─ Individual K-12 schools → schools level
│ └─ See ./references/schools-endpoints.md
├─ School districts / LEAs → school-districts level
│ └─ See ./references/districts-endpoints.md
├─ Colleges / Universities → college-university level
│ └─ See ./references/colleges-endpoints.md
└─ Not sure
├─ Need school-specific data (discipline, AP, demographics) → schools
├─ Need aggregate district data (finance, poverty) → school-districts
└─ Need postsecondary data (enrollment, aid, outcomes) → college-universityResearch topic?
├─ Enrollment / Demographics
│ ├─ K-12 public schools → CCD enrollment endpoints
│ ├─ Civil rights indicators → CRDC enrollment
│ └─ Colleges → IPEDS enrollment
├─ School Finance
│ ├─ District revenue/expenditure → CCD finance
│ └─ College finance → IPEDS finance
├─ Student Outcomes
│ ├─ K-12 assessments → EDFacts
│ ├─ Graduation rates (K-12) → EDFacts
│ ├─ College completion → IPEDS completions
│ └─ Post-college earnings → College Scorecard
├─ Student Aid / Loans
│ ├─ College financial aid → IPEDS aid
│ ├─ Federal loans/grants → FSA
│ └─ Debt/repayment → College Scorecard
├─ Discipline / Civil Rights
│ └─ K-12 discipline, harassment, restraint → CRDC
├─ Poverty Estimates
│ └─ District-level → SAIPE
└─ Directory / Location
├─ K-12 schools → CCD directory
├─ Districts → CCD directory
└─ Colleges → IPEDS directoryFinding variables? ├─ Know the endpoint → Check reference file for variable list ├─ Know the topic → Use topic index below ├─ Need to search programmatically → See ./references/metadata-api.md └─ Need code definitions → See ./references/variable-codes.md
| Level | Key Sources | Primary ID | ID Format | |-------|-------------|------------|-----------| | schools | CCD, CRDC, EDFacts, MEPS, NHGIS | `ncessch` | 12-char string | | school-districts | CCD, SAIPE, EDFacts | `leaid` | 7-char string | | college-university | IPEDS, Scorecard, FSA, PSEO, EADA | `unitid` | 6-digit integer |
| Source | Level | Description | Years | |--------|-------|-------------|-------| | CCD | Schools, Districts | Public K-12 directory, enrollment, finance | 1986-2023 | | CRDC | Schools | Civil rights indicators, discipline, AP courses | 2011-2021 | | EDFacts | Schools, Districts | Assessments, graduation rates | 2009-2020 | | IPEDS | Colleges | Enrollment, completions, finance, institutional data | 1980-2023 | | College Scorecard | Colleges | Earnings, debt, student outcomes | 1996-2020 | | SAIPE | Districts | Census poverty estimates for school-age children | 1995-2023 | | FSA | Colleges | Federal student aid, loans, grants, 90/10 | 1999-2021 | |
📌 文档结构(2026-07-22 起): 本文件是中文默认入口 —— banner + badges + 信任面 + 9 阶段流水线速览 + 76 行合集总表。 每个合集的完整描述、按用途分组、精确数字、验证方法在 docs/CONTENT_ZH.md(扩展正文,总表行内的 → 直接跳转到对应锚点)。 English version: README-en.md · 中文扩展正文:docs/CONTENT_ZH.md · README-zh-CN.md 已弃用(重定向占位) 🌐 语言: English |
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest +…
Use when the user asks to run a full empirical / causal analysis in Python — by default in the style of an applied economics paper (AER / QJE / JPE / ReStud /…
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest +…
Classical end-to-end empirical analysis workflow in the traditional Stata ecosystem — native Stata + reghdfe + ivreg2 + csdid + did_imputation +…
Classical end-to-end empirical analysis workflow in the modern tidyverse + econometrics R ecosystem — dplyr + tidyr + haven + fixest + sandwich + lmtest +…
Systematic writing framework for philosophy and interdisciplinary academic papers from optimized outline to submission-ready manuscript. Use when users want…