pipeline
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy +…
Create new Beamer lecture from papers and materials. Guided workflow with notation consistency.
Claude Code
$ npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill create-lecture --agent claude-code
Codex
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill create-lecture -a codex -y
Cursor
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill create-lecture -a cursor -y
Gemini CLI
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill create-lecture -a gemini-cli -y
GitHub Copilot
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill create-lecture -a github-copilot -y
OpenCode
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill create-lecture -a opencode -y
Windsurf
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill create-lecture -a windsurf -y
name: create-lecture description: Create new Beamer lecture from papers and materials. Guided workflow with notation consistency. argument-hint: "[Topic name]" allowed-tools: ["Read", "Grep", "Glob", "Write", "Edit", "Bash", "Task"] context: fork
Create a beautiful, pedagogically excellent Beamer lecture deck.
**This is a collaborative, iterative process. The instructor drives the vision; Claude is a thinking partner.**
---
1. **Read the knowledge base FIRST** — notation registry, narrative arc, applications database 2. Every new symbol MUST be checked against the notation registry 3. Motivation before formalism — no exceptions 4. Worked example within 2 slides of every definition 5. Max 2 colored boxes per slide 6. No `\pause` or overlay commands (check project rules) 7. Transition slides at major conceptual pivots 8. Thread at least 1 running empirical application throughout 9. All citations verified against the bibliography 10. **Work in batches of 5-10 slides** — share for feedback, don't bulk-dump
---
---
[ ] Lecture compiles without errors [ ] No overfull hbox > 10pt [ ] All citations resolve [ ] Every definition has motivation + worked example [ ] Max 2 colored boxes per slide [ ] 2-3 Socratic questions embedded [ ] Transition slides between sections [ ] At least 1 running application threaded throughout [ ] New notation added to knowledge base [ ] Session log updated [ ] Devil's Advocate run
## 🔥 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…