pipeline
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest +…
Integrate AI tools into economic research, teaching, and policy analysis with attention to privacy and reproducibility.
$ npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill ai-augmented-economist --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/ai-augmented-economistContext preview
The summary Claude sees to decide when to auto-load this skill.
Integrate AI tools into economic research, teaching, and policy analysis with attention to privacy and reproducibility.
name: ai-augmented-economist
description: Integrate AI tools into economic research, teaching, and policy analysis with attention to privacy and reproducibility.
owner: Vera / Personal
tags: [AI, research, teaching]
triggers: ["use AI tools","automate analysis","AI in economics"]
inputs:
- name: project_context
type: string
required: false
outputs:
- name: integration_plan
type: document
- name: data_pipeline_design
type: document
examples:
- prompt: |
Propose an AI integration plan for a small research team analyzing childcare data.
expected: |
Tool choices, privacy safeguards, reproducible pipeline, and SLA for model updates.
guardrails: |
Do not upload personally identifying data to external models; document training data provenance.Purpose
Provide a stepwise approach to choose AI tools, build secure data pipelines, and apply AI methods responsibly in economics work.
Workflow
1. Assess tasks suitable for AI augmentation 2. Select tools considering privacy and licensing 3. Build reproducible data pipelines and artifact tracking 4. Validate outputs against domain knowledge and econometric checks 5. Monitor models and document assumptions
📌 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…