pipeline
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest +…
CSS — annual Clery Act crime/fire safety for Title IV institutions. Portal: hate crimes only (2005-2021); primary offenses, VAWA, arrests, fire safety need ope.ed.gov directly. Use for campus crime analysis. Identified by IPEDS unitid.
$ npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill education-data-source-campus-safety --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/education-data-source-campus-safetyContext preview
The summary Claude sees to decide when to auto-load this skill.
CSS — annual Clery Act crime/fire safety for Title IV institutions. Portal: hate crimes only (2005-2021); primary offenses, VAWA, arrests, fire safety need ope.ed.gov directly. Use for campus crime analysis. Identified by IPEDS unitid.
name: education-data-source-campus-safety description: >- CSS — annual Clery Act crime/fire safety for Title IV institutions. Portal: hate crimes only (2005-2021); primary offenses, VAWA, arrests, fire safety need ope.ed.gov directly. Use for campus crime analysis. Identified by IPEDS unitid. metadata: audience: any-agent domain: data-source skill-authored: "2026-02-09" skill-last-updated: "2026-02-09"
Campus Safety and Security (CSS) — annual Clery Act crime and fire safety reporting for all Title IV postsecondary institutions. Full dataset covers primary offenses, VAWA offenses, hate crimes, arrests/referrals, and fire safety. Portal mirror provides only hate crimes data (2005-2021); primary offenses, VAWA, arrests, and fire safety require direct access to ope.ed.gov/campussafety. Use when analyzing campus hate crimes via Portal or understanding Clery Act reporting structure. Identified by IPEDS unitid.
Guide to understanding and using campus crime and fire safety data collected under the Clery Act.
> **CRITICAL: Value Encoding** > > The Education Data Portal mirrors use **integer codes** for categorical variables, not string labels: > > | Variable | Example Code | Meaning | > |----------|--------------|---------| > | `bias` | `1` | Race-based bias | > | `crime_type` | `14` | Intimidation | > | `fips` | `6` | California | > > See `./references/variable-definitions.md` for complete code mappings.
> **Truth Hierarchy:** When interpreting variable values, apply this priority: > 1. **Actual data file** (what you observe in the parquet/CSV) -- this IS the truth > 2. **Live codebook** (.xls in mirror) -- authoritative documentation, may lag > 3. **This skill documentation** -- convenient summary, may drift from codebook > > If this documentation contradicts the codebook, trust the codebook. If the codebook contradicts observed data, trust the data and investigate. Use `get_codebook_url()` from `fetch-patterns.md` to download codebooks.
The Campus Safety and Security (CSS) data comes from the annual survey required by the Jeanne Clery Disclosure of Campus Security Policy and Campus Crime Statistics Act:
| File | Purpose | When to Read | |------|---------|--------------| | `clery-act.md` | Legal framework, history, reporting requirements | Understanding compliance context | | `crime-categories.md` | Criminal offenses, definitions, classification rules | Interpreting crime statistics | | `vawa-offenses.md` | Dating violence, domestic violence, stalking | Analyzing gender-based violence data | | `hate-crimes.md` | Bias categories, additional offenses, classification | Working with hate crime statistics | | `campus-geography.md` | On-campus, residence halls, public property, noncampus | Understanding location categories | | `fire-safety.md` | Fire statistics, safety systems, HEOA requirements | Analyzing residential fire data | | `arrests-referrals.md` | Drug, alcohol, weapons violations | Working with disciplinary data | | `variable-definitions.md` | Key variables, data structure, identifiers | Building queries | | `limitations.md` | Underreporting, comparability issues, interpretation | Critical data analysis |
Crime data type?
├─ Violent crimes (murder, assault, robbery, sex offenses)
│ └─ See ./references/crime-categories.md
├─ Property crimes (burglary, motor vehicle theft, arson)
│ └─ See ./references/crime-categories.md
├─ Sex offenses (rape, fondling, incest, statutory rape)
│ └─ See ./references/crime-categories.md
├─ Dating/domestic violence, stalking
│ └─ See ./references/vawa-offenses.md
├─ Hate crimes (bias-motivated)
│ └─ See ./references/hate-crimes.md
├─ Drug/alcohol/weapons violations
│ └─ See ./references/arrests-referrals.md
└─ Fire incidents in residence halls
└─ See ./references/fire-safety.mdUnderstanding crime location?
├─ On-campus (main campus buildings/grounds)
│ └─ See ./references/campus-geography.md
├─ On-campus student housing (subset of on-campus)
│ └─ See ./references/campus-geography.md
├─ Public property (streets, sidewalks adjacent to campus)
│ └─ See ./references/campus-geography.md
├─ Noncampus (off-site but institution-controlled)
│ └─ See ./references/campus-geography.md
└─ Not sure which category
└─ See ./references/campus-geography.md (definitions section)Planning institutional comparisons?
├─ Want to rank schools by safety
│ └─ See ./references/limitations.md (CRITICAL: comparability issues)
├─ Comparing similar institution types
│ └─ See ./references/limitations.md (controlling for factors)
├─ Analyzing trends over time
│ └─ See ./references/limitations.md (reporting changes)
└─ Understanding what statistics mean
└─ See ./references/limitations.md (interpretation guidance)| Code | Crime Type | Category | |------|------------|----------| | `1` | Murder/Non-negligent Manslaughter | Primary Offe
📌 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…