pipeline
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy +…
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.
Claude Code
$ npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill education-data-source-ccd --agent claude-code
Codex
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill education-data-source-ccd -a codex -y
Cursor
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill education-data-source-ccd -a cursor -y
Gemini CLI
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill education-data-source-ccd -a gemini-cli -y
GitHub Copilot
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill education-data-source-ccd -a github-copilot -y
OpenCode
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill education-data-source-ccd -a opencode -y
Windsurf
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill education-data-source-ccd -a windsurf -y
name: education-data-source-ccd description: >- 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. metadata: audience: any-agent domain: data-source skill-authored: "2026-02-09" skill-last-updated: "2026-02-09"
Common Core of Data (CCD) — the federal complete-universe database of all U.S. public K-12 schools and districts (~100,000 schools, ~18,000 districts), collecting enrollment, staffing, finance, and directory data annually (1986-present). Use when analyzing public school enrollment by grade/race/sex, district finances, school staffing, or directory attributes. Public schools and districts only; excludes private schools and postsecondary. Note significant variable encoding and race/ethnicity definition changes over time.
The CCD is the Department of Education's comprehensive, annual, national database of all public elementary and secondary schools and school districts in the United States. It is the only federal dataset that provides a complete universe census (not a sample) of U.S. public K-12 education.
> **CRITICAL: Value Encoding** > > The Education Data Portal uses **integer codes** for categorical variables that > differ from NCES's original string codes. Always verify codes against codebooks. > > | Context | `school_type` | `charter` | `urban_centric_locale` | > |---------|---------------|-----------|------------------------| > | **Portal (integers)** | `1` (Regular) | `0` (No) / `1` (Yes) | `11` (City-Large) | > | NCES original | `1-Regular school` | `Yes` / `No` | `11-City: Large` | > > **Note:** `charter` and `magnet` use `0/1` encoding, NOT `1=Yes / 2=No` as some NCES documentation shows. > > See `./references/variable-definitions.md` for complete encoding tables.
| File | Purpose | When to Read | |------|---------|--------------| | `survey-components.md` | Detailed coverage of each CCD survey component | Understanding what data is collected | | `data-collection.md` | How data flows from schools to NCES, timelines, respondent universe | Understanding data provenance and timing | | `variable-definitions.md` | Key variables, coding schemes, special values | Interpreting specific data elements | | `data-quality.md` | Missing data patterns, suppression, state variations | Assessing data reliability | | `historical-changes.md` | Definition changes, code revisions over time | Longitudinal analysis |
What information do you need?
├─ School/district names, addresses, contacts → Directory
│ └─ See ./references/survey-components.md#directory
├─ Student enrollment counts → Membership
│ ├─ By grade → Membership (grade disaggregation)
│ ├─ By race/ethnicity → Membership (race disaggregation)
│ ├─ By sex → Membership (sex disaggregation)
│ └─ See ./references/survey-components.md#membership
├─ Staff/teacher counts → Staffing
│ └─ See ./references/survey-components.md#staffing
├─ Revenue and expenditure → Finance
│ ├─ State-level totals → National Public Education Financial Survey
│ ├─ District-level detail → School District Finance Survey (F-33)
│ └─ See ./references/survey-components.md#finance
├─ Graduation/dropout rates → Dropout and Completers
│ └─ See ./references/survey-components.md#dropout-completers
└─ School type, charter status, locale → Directory
└─ See ./references/survey-components.md#directoryUnexpected data values?
├─ Negative numbers (-1, -2, -3, -9) → Missing data codes
│ └─ See ./references/variable-definitions.md#missing-data-codes
├─ Very different from prior year → Check for definition changes
│ └─ See ./references/historical-changes.md
├─ State appears as outlier → Check state-specific reporting
│ └─ See ./references/data-quality.md#state-variations
├─ Large number of zeros → Check suppression rules
│ └─ See ./references/data-quality.md#suppression
└─ Locale codes don't match → Pre/post 2006 locale system change
└─ See ./references/historical-changes.md#locale-codesBuilding a time series?
├─ Race/ethnicity categories → Major change in 2010
│ └─ See ./references/historical-changes.md#race-ethnicity
├─ Locale codes → Completely revised in 2006
│ └─ See ./references/historical-changes.md#locale-codes
├─ School/district IDs → Check for ID changes
│ └─ See ./references/variable-definitions.md#identifiers
├─ Free/reduced lunch → CEP and direct certification changes
│ └─ See ./references/data-quality.md#frpl
└─ Finance data → Definition changes and inflation
└─ See ./references/historical-changes.md#finance| Component | Level | Key Variables | Years | Update Cycle | |-----------|-------|---------------|-------|--------------| | Directory | School, LEA, State | Name, address, type, status, locale, charter | 1986+ | Annual | | Membership | School, LEA, State | Enrollment by grade, race, sex | 1986+ | Annual | | Staffing | School
## 🔥 力荐 · StatsPAI — Stata 与 R 的 Python 平替,为 AI Agent 而生 🔁 Stata / R 平替:reghdfe、csdid、rdrobust、synth、fixest、did 等常用命令在 Python 中一站式替代,一个 import statspai as sp 搞定 DID / RD / IV / SCM / DML / matching 🤖 面向 Agent 设计:结构化结果对象 + 内置 MCP Server,Claude / Codex
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy +…
Use when the user asks to run a full empirical / causal analysis in Python — by default in the style of an applied economics…
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy +…
Classical end-to-end empirical analysis workflow in the traditional Stata ecosystem — native Stata + reghdfe + ivreg2 + csdid +…
Classical end-to-end empirical analysis workflow in the modern tidyverse + econometrics R ecosystem — dplyr + tidyr + haven +…
Systematic writing framework for philosophy and interdisciplinary academic papers from optimized outline to submission-ready…
Direct browser control via CDP for web interaction: automation, scraping, testing, screenshots, and site/app work. Also covers…
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling…