pipeline
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest +…
Interpretation guidance for Urban Institute Portal datasets. Coded values (-1/-2/-3), year definitions, grade encoding, suppression, licensing, cross-source joins. Use when interpreting Portal data before analysis. Routes to source-specific skills.
$ npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill education-data-context --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/education-data-contextContext preview
The summary Claude sees to decide when to auto-load this skill.
Interpretation guidance for Urban Institute Portal datasets. Coded values (-1/-2/-3), year definitions, grade encoding, suppression, licensing, cross-source joins. Use when interpreting Portal data before analysis. Routes to source-specific skills.
name: education-data-context description: >- Interpretation guidance for Urban Institute Portal datasets. Coded values (-1/-2/-3), year definitions, grade encoding, suppression, licensing, cross-source joins. Use when interpreting Portal data before analysis. Routes to source-specific skills. metadata: audience: any-agent domain: data-documentation
Data origin, caveats, and interpretation guidance for Urban Institute Education Data Portal datasets. Use when interpreting Portal coded values (-1/-2/-3 missing/not-applicable/suppressed), understanding year definitions (fall vs. academic year), applying correct grade encoding (grade=-1 means Pre-K, not missing), assessing suppression rates, citing data under ODC-By license, or reviewing any Portal data before analysis. Also covers joining identifiers across CCD, IPEDS, CRDC, and other sources, and routes to source-specific deep-dive skills.
This skill provides critical context for interpreting data from the Urban Institute Education Data Portal. Education data has source-specific limitations that can significantly affect analysis validity.
All education data currently accessible through this system is obtained from the **Urban Institute Education Data Portal (EDP)**, not directly from original source agencies (NCES, Census Bureau, Department of Education, etc.). The EDP is a curation and standardization layer that:
Each `education-data-source-*` skill documents what is available through the Portal for that source, including any known gaps relative to the original data collection. When a skill also documents variables or components only available from the original source directly, this is clearly noted.
> **Note:** This provenance applies specifically to the current education data source skills. Future data source skills may access data from other providers with different characteristics.
| File | Content | When to Read | |------|---------|--------------| | `./references/ccd-context.md` | K-12 schools/districts caveats | After pulling CCD data | | `./references/ipeds-context.md` | College/university caveats | After pulling IPEDS data | | `./references/crdc-context.md` | Civil rights data caveats | After pulling CRDC data | | `./references/scorecard-context.md` | College Scorecard caveats | After pulling Scorecard data | | `./references/edfacts-context.md` | Assessment/graduation caveats | After pulling EDFacts data | | `./references/data-relationships.md` | Joining tables, identifiers | When merging datasets |
These skills document both EDP-available data and original source context. Each skill notes when content applies only to the original source (not available through the Portal).
For comprehensive understanding beyond the quick context files above, load the dedicated data source skill:
| Data Source | Deep-Dive Skill | Key Deep Topics | |-------------|-----------------|-----------------| | CCD | `education-data-source-ccd` | Survey components, EDFacts submission, state variations, historical changes | | CRDC | `education-data-source-crdc` | Civil rights legal context, underreporting issues, year-to-year evolution | | EDFacts | `education-data-source-edfacts` | ESSA/NCLB context, why states aren't comparable, ACGR methodology | | IPEDS | `education-data-source-ipeds` | All 12+ surveys, graduation rate population limits, GASB vs FASB | | Scorecard | `education-data-source-scorecard` | IRS earnings methodology, Title IV selection bias, suppression rules | | SAIPE | `education-data-source-saipe` | Model-based estimation, no district confidence intervals | | FSA | `education-data-source-fsa` | Title IV programs, financial responsibility scores, 90/10 rule | | MEPS | `education-data-source-meps` | Superior to FRPL for cross-state poverty comparison | | NHGIS | `education-data-source-nhgis` | Census geography links, boundary changes over time | | NACUBO | `education-data-source-nacubo` | Endowment study methodology, voluntary participation bias | | NCCS | `education-data-source-nccs` | Form 990 data, NTEE codes, private college relevance | | EADA | `education-data-source-eada` | Title IX context, not same as compliance data | | Campus Safety | `education-data-source-campus-safety` | Clery Act, underreporting, geography definitions | | PSEO | `education-data-source-pseo` | LEHD methodology, experimental status, state coverage |
**When to load deep-dive skills:**
📌 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…