复制安装命令
用 Codex 或 Claude 安装复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它先审查 Skill 页面再帮你安装。
复制前请先查看来源、License 和安全提示。
Security audit: baseline 52/52 CLEAN
用 Codex 或 Claude 安装复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它先审查 Skill 页面再帮你安装。
复制前请先查看来源、License 和安全提示。
来源文件:README.md
📌 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 | 简体中文(默认) | 繁體中文 | 日本語 | 한국어
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Stanford REAP × CoPaper.AI · 实证研究 AI 工具的学术工业级产品
由斯坦福实证研究方法论团队打造,覆盖从数据清洗到顶刊投稿的完整工作流
🚀 New here? Open the Skill Search → to filter all 1,096 skills by method, stage, language, and license. The 5-minute tour (
make quickstart) prints the same picture in your terminal.🇨🇳 中文用户从本文件开始(流水线速览 + 76 行总表),每个合集的完整描述见
docs/CONTENT_ZH.md。📖 English readers: seeREADME-en.md.
| Rigor lane | Count | Where |
|---|---|---|
| Numeric benchmark tasks — gold values recomputed from real data each run | 17 | benchmark/ |
| Behavioral eval scenarios / rubric items | 37 / 183 | eval-harness/ |
Full trust overview:
docs/TRUST.md·docs/RIGOR_COVERAGE.md
中文内容分两级维护,各司其职:
docs/CONTENT_ZH.md(扩展正文):每个合集的完整描述(#skill-NN 锚点)、按用途分组、精确数字、2 分钟验证、三层信任、旗舰流水线详解、贡献与引用。总表行内的 → 直接跳到对应锚点。README-en.md · README-zh-TW.md · README-ja.md · README-ko.md[!NOTE] 维护规则: 改合集总表 → 本文件与 CONTENT_ZH.md 的锚点表两处同步;改合集详情 / 分组 / 数字 → 只改
docs/CONTENT_ZH.md。统计数字(合集数 / skill 数)以catalog/skills.json为准,由make validate的 readme-stats 检查器守护。贡献者(Contributors): 提交前请在本地跑通完整门禁
make check(catalog 校验 + 链接 + 单元测试 + eval-harness + benchmark)。详见CONTRIBUTING.md。旧版归档:
README-zh-CN.md已弃用,仅作向后兼容的重定向占位。
AERS 不只是 76 个散装 skill —— 它能陪你走完一篇论文。 从模糊 idea → 选题精炼 → 文献综述 → 数据获取 → 识别策略 → 估计建模 → 稳健性审计 → 出版级表格 / 图形 → 写作与同行评审 → 降 AIGC → 投稿。端到端、全自动、每一步都可被人介入(中间任何一步你都可以接过去手工改方法、补变量、加稳健性,再让流水线自动接上跑)。
Paper-WorkFlow 是 AERS 的"指挥棒",它把上面 9 个阶段的 skill 串成 一条按键即运行的端到端流水线。
你在 IDE 入口给它一句自然语言:
"开一个新论文项目:空气污染与中国劳动力市场,CS 设计 + 省级面板"
它会自动按顺序调:
sp.csdid(...) 给出 CS-DID 估计草案 + 写出估计方程与识别假设sp.feols(...) + sp.honest_did(...)任何阶段你都可以手动介入 —— 上一阶段的产物全部落盘(产物-幂等 pipeline),你接过去改方法、补控制、加稳健性,再让流水线自动接下去跑。这就是"全自动 + 可介入"。
| ⭐ Skill | 在流水线里的角色 |
|---|---|
| 00 StatsPAI 🔥 | 因果引擎:900+ 函数,sp.causal(...) 一行跑闭环(DID / RD / IV / SCM / DML / matching) |
| 00.1 Full Empirical · Python 📘 | 显式 Python 栈(pandas / statsmodels / linearmodels / pyfixest) |
| 00.2 Full Empirical · Stata 📊 | 显式 Stata 栈(reghdfe / ivreg2 / csdid / sdid / rdrobust) |
| 00.3 Full Empirical · R 📗 | 显式 R 栈(tidyverse / fixest / did / HonestDiD)+ Quarto 渲染 |
| 48 de-AIGC-skills 🇨🇳🇬🇧 | 中英双语学术降 AIGC(Turnitin AI / GPTZero / 知网 / 万方) |
| 50 AER-skills 📕 | Top-5 经济学投稿套件:识别 → 稳健性 → R&R |
| 69 Paper-WorkFlow 🧭 | 元编排器,把上面 9 个阶段串成一键流水线 |
为什么挑这 7 个?因为它们的行为都被基准钉死了 —— 不是营销口径,是对着已知答案反复跑过验证过的(17 项数值 benchmark + 37 项行为评测 ↗)。
↴ 直跳到下方 76 行总表(每个合集带 #skill-NN 锚点)。如果你更关心"这些 skill 怎么用"而不是"有哪些 skill",看 📘 中文唯一权威正文 里的「按用途分组」与「旗舰流水线」两节。
00 → 72,编号连续无空缺)打开仓库 → 看见整座库。 全部 76 个合集 · 1,096 个 skill,每一个都已 vendor 进本仓库,由
catalog/skills.json跟踪。⭐ = Stanford REAP × CoPaper.AI 团队自研的 skill;其余为精选、经安全审计的社区作品。主题图例 — 🚀 全流程与编排器 · 🎯 因果推断与计量经济学 · 📚 文献与研究设计 · ✍️ 写作 / 编辑 / 去 AIGC · 📑 引用 / 复现 / 同行评审 · 🛠️ 数据 / 工具 / 基础设施
点击【→】 跳转到
docs/CONTENT_ZH.md中该合集的完整描述;点击合集名 直接打开其目录。
| # | 合集 | 一句话 | 详情 |
|---|---|---|---|
| ⭐ 00 | StatsPAI 🔥 | 因果引擎 · Agent-native Python DSL:sp.causal(...) 一行跑闭环(DID/RD/IV/SCM/DML,900+ 函数) | → |
| ⭐ 00.1 | Full Empirical · Python 📘 | 显式栈:pandas · statsmodels · linearmodels · pyfixest | → |
| ⭐ 00.2 | Full Empirical · Stata 📊 | reghdfe · ivreg2 · csdid · sdid · rdrobust 复现包 | → |
| ⭐ 00.3 | Full Empirical · R 📗 | tidyverse · fixest · did · HonestDiD + Quarto 渲染 | → |
| 01 | academic-paper-skills | 大纲 → 手稿写作 + 7 维审稿人模拟 | → |
| 02 | research-skills | 医学影像综述、提案、论文转幻灯片 | → |
| 03 | scientific-skills | 假设生成 + 28 个科学数据库 | → |
| 04 | scientific-writer | 引用管理 + 科学写作 | → |
| 05 | research-superpower | 系统化检索、筛选与引文溯源 | → |
| 06 | stats-paper-writing | 端到端 LaTeX 统计论文写作 | → |
| 07 | AI-Research-SKILLs | 发表级 ML 图表、LaTeX、引文核验 | → |
| 08 | latex-document-skill | 创建 / 编译任意 LaTeX 文档为 PDF | → |
| 09 | awesome-econ-ai | Python 面板数据分析(linearmodels) | → |
| 10 | causal-inference-mixtape | DID / IV / RDD / SCM 模板(Cunningham) | → |
| 11 | compound-science | 面向定量社会科学的贝叶斯估计 | → |
| 12 | claude-code-my-workflow | 提交 → PR → 合并的研究工作流(Emory) | → |
| 13 | MixtapeTools | Cunningham 的因果推断工具集与讲义 | → |
| 14 | research-starter | R 中的 IV / DiD / RDD,含完整诊断 | → |
| 15 | social-science-research | R 或 Python 端到端数据分析 | → |
| 16 | clo-author | 多代理数据分析(R / Stata / Python) | → |
| 17 | DAAF | 安全意识代理框架(32 条 deny rule) | → |
| 18 | stata-accounting | 来自 126 篇 JAR 论文的实测 Stata 范式 | → |
| 19 | vera-economic-intelligence | 经济情报 / 政策研究情报工作流 | → |
| 20 | python-econ-skill | DSGE / HANK 与定量经济计算 | → |
| 21 | AI-research-feedback | 用 AI 同行评审生成结构化反馈 | → |
| 22 | christopherkenny-skills | 面向 Quarto(.qmd)的 APSA 风格检查器 | → |
| 23 | baygent | 带护栏的 PyMC / Arviz 贝叶斯工作流 | → |
| 24 | academic-research-skills | 5 审稿人多视角论文评审 | → |
| 25 | Diverga | 研究问题精炼器(抗模式坍缩) | → |
| 26 | scholar | 统计算法设计与文档 | → |
| 27 | my_claude_skills | 经济学摘要写作指南 | → |
| 28 | paper-replicate-agent | 论文复现代理演示 | → |
| 29 | project20XXy | 可复现手稿 + notebook 项目 | → |
| 30 | zirui-song-claude-skills | Zirui Song 的研究辅助 Claude 技能集 | → |
| 31 | claude-code-skills | Python 面板数据分析 | → |
| 32 | stata-skill | 高性能 Stata C/C++ 插件 | → |
| 33 | claude-scholar | 研究全生命周期:选题 → 综述 → 实验 → 审稿回复 | → |
| 34 | research-companion | 头脑风暴、评估并决策研究方向 | → |
| 35 | academic-writing-skills | 面向投稿场所的工业 AI 文献研究 | → |
| 36 | literature-review-skill | 完整文献综述工作流(中文) | → |
| 37 | IlanStrauss-ai-skills | Ilan Strauss 经济学研究 AI 工作流 | → |
| 38 | academic-proofreader | 学术校对 | → |
| 39 | marginaleffects | 预测、斜率与比较(R / Python) | → |
| 40 | pyfixest | Python 中的快速固定效应估计 | → |
| 41 | sewage-econometrics-check | 10 项复现包审计 | → |
| 42 | ARIS | 自主「research-in-sleep」代理,端到端 | → |
| 43 | research-plugins | 478 个研究插件:数据可视化、领域、基础设施 | → |
| 44 | humanizer_academic | 为医学/学术手稿去 AI 味(23 类模式) | → |
| 45 | deslop | 去除 AI 写作痕迹(5 维评分) | → |
| 46 | stop-slop | 三层 AI 痕迹检测与改写 | → |
| 47 | avoid-ai-writing | 审计 → 改写 → 二次审计 AI 味(留痕) | → |
| ⭐ 48 | de-AIGC-skills 🇨🇳🇬🇧 | 中英双语学术降 AIGC(Turnitin AI / GPTZero / 知网 / 万方) | → |
| 49 | humanize-chinese | 检测并人性化 AI 生成的中文文本 | → |
| ⭐ 50 | AER-skills 📕 | Top-5 经济学投稿套件:识别 → 稳健性 → R&R | → |
| 51 | CausalPy | 贝叶斯准实验(PyMC Labs) | → |
| 52 | slr-prisma | 系统文献综述,PRISMA 2020 | → |
| 53 | thematic-analysis | Braun & Clarke 六阶段定性主题分析 | → |
| 54 | open-science-skills | 引用一致性、DOI 与论据支撑审计 | → |
| 55 | r-skills | R 中用 brms 做贝叶斯推断 | → |
| 56 | econ-writing-skill | 综合 50+ 顶级指南的经济学写作 | → |
| 57 | edgartools | 查询与分析 SEC 文件 | → |
| 58 | econstack | 政策简报(UK GES / AU Treasury) | → |
| 59 | openalex-skill | 通过 OpenAlex 查询 2.4 亿+ 学术作品 | → |
| 60 | superpapers | 综合性实证研究支持套件 | → |
| 61 | research-methods | 与预注册匹配的验证性检验 | → |
| 62 | citation-checker | 对照 CrossRef / S2 / OpenAlex 核验引用 | → |
| 63 | scientific-agent-skills | DoWhy 识别–估计–反驳框架 | → |
| 64 | mcp-stata | 20 个 Stata 因果推断与复现 skill | → |
| 65 | game-theory-paper-writer | 生成并压力测试博弈论论文 | → |
| 66 | empirical-research-skills | 面向大型面板的 R 性能优化 | → |
| 67 | econfin-workflow-toolkit | 中国公司金融实证工作流,从提案到论文 | → |
| 68 | research-productivity-skills | 论文检索、SSRN、DOI 查询、下载 | → |
| ⭐ 69 | Paper-WorkFlow 🧭 | 元编排器,串起整个社会科学论文流水线 | → |
| 70 | ssci-polish ✍️ | SSCI / SCI 英文论文语言润色(语法、可读性、学术语气) | → |
| ⭐ 71 | lit-review-agent-tools 🔍 | 文献综述工具选型 + 一键安装运行(MinerU / PaperQA2 / ASReview / STORM / MCP 服务器) | → |
| ⭐ 72 | Kaggle Research 🧪 | 通过官方 CLI 安全检索 Kaggle 资源、限界下载公开数据并保留审计证据 | → |
想看更详细的描述(主题分类、字段、统计)? 见
docs/CONTENT_ZH.md中标注#skill-NN锚点的同一张表 —— 它是每个合集的完整描述所在的扩展正文。
自 2026-04 首次发布以来的主干里程碑(完整提交记录见 Commits 与 CHANGELOG.md):
---
config:
gitGraph:
rotateCommitLabel: false
---
gitGraph TB:
commit id: "2026-04 首次发布"
branch community
commit id: "2026-05 首个社区 PR"
checkout main
merge community
commit id: "2026-05 更名 AERS"
commit id: "2026-06 插件市场"
commit id: "2026-06 全库路由器"
commit id: "2026-07 首个 tag" tag: "v2026.07"
branch kaggle
commit id: "2026-07 Kaggle 集成"
checkout main
merge kaggle
commit id: "2026-08 de-AIGC 双语"
Star 增长曲线(非提交数)· 由 scripts/build-star-history.py 从 GitHub API 生成并提交入库
如果 AERS 对你的工作有帮助,请引用它(CITATION.cff)并点个 Star,让更多研究者看到。
AI 是放大器,不是替代品。它替你做最耗时的"搬砖",你保留最核心的"判断"。
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Stanford REAP × CoPaper.AI · 实证研究 AI 工具的学术工业级产品
![]() 扫码访问 copaper.ai |
![]() 关注公众号「CoPaper.AI」 |
内置 20 个方法论 skill · 20 分钟完成实证论文 · 自研 StatsPAI(900+ 函数 / MIT 开源)
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-accessDiscovers 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.
education-data-query skill-1, -2, -3)education-data-source-* skills for source-specific coverage detailsSkill Provenance Note: Each
*-data-source-*skill includesprovenance.skill_last_updatedin 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-queryskill for fetch patterns anddatasets-reference.mdfor 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-university
Research 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 directory
Finding 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 |
| MEPS | Schools | School poverty measure | 2006-2019 |
| NHGIS | Schools | Census geography crosswalks | 1990, 2000, 2010, 2020 |
| Endpoint | Description |
|---|---|
/schools/ccd/directory/{year}/ | School directory (location, type, enrollment) |
/schools/ccd/enrollment/{year}/{grade}/ | Enrollment by grade |
/schools/crdc/discipline/{year}/ | Discipline incidents |
/schools/crdc/ap-ib-enrollment/{year}/race/sex/ | AP/IB enrollment (requires disaggregation) |
/schools/edfacts/assessments/{year}/{grade}/ | Assessment results |
| Endpoint | Description |
|---|---|
/school-districts/ccd/directory/{year}/ | District directory |
/school-districts/ccd/enrollment/{year}/{grade}/ | District enrollment |
/school-districts/ccd/finance/{year}/ | Revenue and expenditure |
/school-districts/saipe/{year}/ | Poverty estimates |
| Endpoint | Description |
|---|---|
/college-university/ipeds/directory/{year}/ | Institution directory |
/college-university/ipeds/admissions-enrollment/{year}/ | Admissions data |
/college-university/ipeds/enrollment-full-time-equivalent/{year}/ | FTE enrollment |
/college-university/ipeds/fall-enrollment/{year}/{level}/ | Fall enrollment |
/college-university/ipeds/graduation-rates/{year}/ | Graduation rates |
/college-university/scorecard/earnings/{year}/ | Post-college earnings |
Follow these steps to identify data for a research question:
Identify data level
Identify relevant data source(s)
Check available endpoints
Review variables and filters
Check years available
Understand source context (RECOMMENDED)
education-data-source-* skill for deep contextPlan query
education-data-query skill for query constructionAll endpoints follow this pattern:
/api/v1/{level}/{source}/{topic}/{year}/[{disaggregation}/]
Examples:
/api/v1/schools/ccd/directory/2022//api/v1/schools/ccd/enrollment/2022/grade-5//api/v1/schools/ccd/enrollment/2022/grade-5/race//api/v1/school-districts/ccd/finance/2021//api/v1/college-university/ipeds/fall-enrollment/2022/undergraduate/| Parameter | Description | Example |
|---|---|---|
fips | State FIPS code | ?fips=6 (California) |
leaid | District ID | ?leaid=0600001 |
ncessch | School ID | ?ncessch=060000100001 |
unitid | College ID | ?unitid=110635 |
year | Filter by year | ?year=2022 |
{
"count": 12345,
"next": "https://educationdata.urban.org/api/v1/...?page=2",
"previous": null,
"results": [
{"ncessch": "...", "school_name": "...", ...},
...
]
}
| Skill | Purpose | When to Use |
|---|---|---|
education-data-query | Download data from mirrors | After identifying endpoints/variables |
education-data-context | Interpret data, understand limitations | After retrieving data |
For comprehensive understanding of each data source beyond the portal documentation, load the appropriate source-specific skill:
| Skill | Data Source | Key Topics |
|---|---|---|
education-data-source-ccd | Common Core of Data | K-12 directory, enrollment, finance, staffing surveys |
education-data-source-crdc | Civil Rights Data Collection | Discipline, harassment, course access, civil rights context |
education-data-source-saipe | Small Area Income & Poverty | District poverty estimates, model methodology |
education-data-source-edfacts | EDFacts | State assessments, graduation rates, accountability |
education-data-source-ipeds | IPEDS | College enrollment, graduation, finance, completions |
education-data-source-scorecard | College Scorecard | Post-college earnings, debt, repayment |
education-data-source-nhgis | NHGIS | Census geography, demographic crosswalks |
education-data-source-fsa | Federal Student Aid | Pell, loans, financial responsibility, 90/10 |
education-data-source-nacubo | NACUBO | College endowment data |
education-data-source-nccs | NCCS | Nonprofit data for private colleges |
education-data-source-meps | MEPS | Model-based school poverty (superior to FRPL) |
education-data-source-eada | EADA | College athletics equity data |
education-data-source-campus-safety | Campus Safety | Campus crime, Clery Act data |
education-data-source-pseo | PSEO | Post-graduation employment outcomes |
When to load source skills:
| Topic | Reference File | Section |
|---|---|---|
| School directory | schools-endpoints.md | CCD Directory |
| School enrollment | schools-endpoints.md | CCD Enrollment |
| Discipline data | schools-endpoints.md | CRDC Discipline |
| AP/IB courses | schools-endpoints.md | CRDC AP-IB-GT |
| K-12 assessments | schools-endpoints.md | EDFacts |
| District directory | districts-endpoints.md | CCD Directory |
| District finance | districts-endpoints.md | CCD Finance |
| District poverty | districts-endpoints.md | SAIPE |
| College directory | colleges-endpoints.md | IPEDS Directory |
| College enrollment | colleges-endpoints.md | IPEDS Enrollment |
| College graduation | colleges-endpoints.md | IPEDS Graduation |
| Financial aid | colleges-endpoints.md | IPEDS Aid, FSA |
| Post-college earnings | colleges-endpoints.md | Scorecard |
| Student debt | colleges-endpoints.md | Scorecard, FSA |
| State FIPS codes | variable-codes.md | State FIPS |
| Grade codes | variable-codes.md | Grade Codes |
| Race/ethnicity codes | variable-codes.md | Race Codes |
| Locale codes | variable-codes.md | Urban-Centric Locale |
| Programmatic discovery | metadata-api.md | All |
Research question: "What is the relationship between school poverty and AP course offerings in California high schools?"
Data level: Schools (individual school records)
Data sources needed:
Endpoints:
/schools/crdc/ap-ib-enrollment/{year}/race/sex/ - AP enrollment (requires disaggregation)/schools/meps/{year}/ - School poverty measureKey variables:
ncessch - school identifier (for joining)fips=6 - California filterYears: Check overlap (CRDC: 2011-2021, MEPS: 2006-2019)
Next step: Load education-data-query skill to construct the actual API calls
ncessch (12-char), leaid (7-char), unitid (6-digit)The Education Data Portal API often uses different variable names than documentation suggests. Always fetch a sample first:
# Test query to verify actual column names
response = requests.get(
"https://educationdata.urban.org/api/v1/college-university/ipeds/directory/2023/"
)
data = response.json()
print("Actual columns:", list(data['results'][0].keys()))
Known discrepancies:
| Documented | Actual API Field | Endpoint |
|---|---|---|
inst_level | institution_level | IPEDS Directory |
applicants_total | number_applied | IPEDS Admissions |
admissions_total | number_admitted | IPEDS Admissions |
grad_rate_150pct | completion_rate_150pct | IPEDS Graduation Rates |
school_poverty | meps_poverty_pct | MEPS |
population_5_17_poverty | est_population_5_17_poverty | SAIPE |
See the relevant education-data-source-* skill for comprehensive variable mappings per source.
The metadata API has undocumented limitations:
?section=schools works to filter by data level?source=ipeds does NOT work - filter client-side insteadsource is actually class_name, source_name is actually labelQuick summaries below. For comprehensive documentation including methodology, variable definitions, data quality issues, and historical changes, load the corresponding education-data-source-* skill.
Coverage: All public elementary and secondary schools and districts in the U.S.
| Topic | Schools | Districts |
|---|---|---|
| Directory | Yes | Yes |
| Enrollment | Yes (by grade, race, sex) | Yes (by grade, race, sex) |
| Finance | No | Yes (revenue, expenditure) |
Key Variables:
ncessch: 12-character NCES school IDleaid: 7-character NCES district IDenrollment: Total enrollment countfree_or_reduced_price_lunch: FRPL-eligible students (poverty proxy)charter: Charter school indicatorurban_centric_locale: Urban/suburban/town/rural classificationDeep dive: Load education-data-source-ccd for survey components, data collection process, variable coding, state variations, and historical changes (e.g., 2006 locale code revision, 2010 race category changes).
Coverage: Biennial survey of public schools (2011, 2013, 2015, 2017, 2020, 2021)
Topics:
Key Feature: Disaggregation by race, sex, disability, and LEP status
Deep dive: Load education-data-source-crdc for civil rights legal context (Title VI, IX, Section 504), collection methodology, underreporting issues, and year-to-year changes.
Coverage: State assessment and accountability data
Topics:
Key Feature: Data available by special populations (disability, economically disadvantaged, LEP, homeless, migrant, foster care)
CRITICAL: State assessment scores CANNOT be compared across states (different tests, cut scores).
Deep dive: Load education-data-source-edfacts for ESSA/NCLB accountability context, why cross-state comparison is invalid, ACGR methodology, and subgroup reporting rules.
Coverage: All Title IV-eligible postsecondary institutions
Topics:
Key Variables:
unitid: 6-digit IPEDS institution IDinst_control: 1=Public, 2=Private nonprofit, 3=Private for-profitinstitution_level: 1=Less-than-2-year, 2=2-year, 4=4-year (no code 3)hbcu: Historically Black college indicatorDeep dive: Load education-data-source-ipeds for critical graduation rate limitations (first-time full-time only), GASB vs FASB finance accounting, survey components, and identifier changes.
Coverage: Title IV institutions with outcome data
Topics:
Key Feature: Links education to labor market outcomes
CRITICAL: Only covers Title IV aid recipients (selection bias toward lower-income students).
Deep dive: Load education-data-source-scorecard for earnings methodology (IRS data), population coverage limitations, suppression rules, and field-of-study data.
Coverage: Census Bureau poverty estimates for school districts
Key Variables:
population_5_17_poverty: Children 5-17 in povertypopulation_5_17_poverty_pct: Percent in povertymedian_household_income: District median incomeDeep dive: Load education-data-source-saipe for model-based estimation methodology, confidence intervals (not available at district level), and comparison to other poverty measures.
Coverage: Title IV institutions receiving federal aid
Topics:
Deep dive: Load education-data-source-fsa for Title IV program details, financial responsibility composite scores, and 90/10 rule compliance.
| Source | Coverage | Deep Dive Skill |
|---|---|---|
| MEPS | School-level poverty estimates (superior to FRPL for cross-state comparison) | education-data-source-meps |
| NHGIS | Census geography crosswalks for schools | education-data-source-nhgis |
| NACUBO | College endowment data | education-data-source-nacubo |
| NCCS | Nonprofit data for private colleges (Form 990) | education-data-source-nccs |
| EADA | College athletics equity data | education-data-source-eada |
| Campus Safety | Campus crime statistics (Clery Act) | education-data-source-campus-safety |
| PSEO | Post-graduation employment outcomes | education-data-source-pseo |
Join school data across sources using ncessch:
| Source 1 | Source 2 | Join Key | Use Case |
|---|---|---|---|
| CCD | CRDC | ncessch | Enrollment + discipline |
| CCD | EDFacts | ncessch | Directory + assessments |
| CCD | MEPS | ncessch | Enrollment + poverty |
| CRDC | MEPS | ncessch | AP courses + poverty |
Note: Match on year when joining (years may not align perfectly)
Join district data using leaid:
| Source 1 | Source 2 | Join Key | Use Case |
|---|---|---|---|
| CCD Directory | CCD Finance | leaid | Characteristics + spending |
| CCD | SAIPE | leaid | Enrollment + poverty |
| CCD | EDFacts | leaid | Enrollment + outcomes |
Join college data using unitid:
| Source 1 | Source 2 | Join Key | Use Case |
|---|---|---|---|
| IPEDS Directory | IPEDS Finance | unitid | Characteristics + finance |
| IPEDS | Scorecard | unitid | Enrollment + earnings |
| IPEDS | FSA | unitid | Enrollment + aid data |
Some disaggregations are part of the URL path:
/schools/ccd/enrollment/{year}/{grade}/ # By grade
/schools/ccd/enrollment/{year}/{grade}/race/ # By grade and race
/schools/ccd/enrollment/{year}/{grade}/race/sex/ # By grade, race, and sex
Other filters are query parameters:
?fips=6 # California only
?charter=1 # Charter schools only
?school_level=3 # High schools only
?urban_centric_locale=11 # Large cities only
| Source | Grade | Race | Sex | Disability | Econ Status | LEP |
|---|---|---|---|---|---|---|
| CCD | Yes | Yes | Yes | No | No | No |
| CRDC | No | Yes | Yes | Yes | No | Yes |
| EDFacts | Yes | Yes | Yes | Yes | Yes | Yes |
| IPEDS | Level | Yes | Yes | No | No | No |
| Source | Earliest | Latest | Update Frequency |
|---|---|---|---|
| CCD Directory | 1986 | 2023 | Annual |
| CCD Finance | 1989 | 2021 | Annual (2-year lag) |
| CRDC | 2011 | 2021 | Biennial |
| EDFacts | 2009 | 2020 | Annual |
| IPEDS | 1980 | 2023 | Annual |
| Scorecard | 1996 | 2020 | Annual |
| SAIPE | 1995 | 2023 | Annual |
| FSA | 1999 | 2021 | Annual |
| Scenario | Data Sources | Key Variables |
|---|---|---|
| Charter vs traditional school outcomes | CCD directory + EDFacts assessments | charter, read_test_pct_prof_midpt |
| College affordability by income | IPEDS directory + net-price-by-income | inst_control, income_level, avg_net_price |
| Discipline disparities by race | CRDC discipline + enrollment (by race) | race, oss_one, expulsions_* |
| Spending and graduation rates | CCD finance + EDFacts grad-rates | exp_current_per_pupil, grad_rate_midpt |
| School poverty and AP access | CRDC ap-ib-enrollment + MEPS | ap_enrollment, meps_poverty_pct |
| College earnings by major | IPEDS completions + Scorecard earnings | cip_code, earn_median_wne_p10 |
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