pipeline
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy +…
Integrate AI tools into economic research, teaching, and policy analysis with attention to privacy and reproducibility.
Claude Code
$ npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill ai-augmented-economist --agent claude-code
Codex
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill ai-augmented-economist -a codex -y
Cursor
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill ai-augmented-economist -a cursor -y
Gemini CLI
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill ai-augmented-economist -a gemini-cli -y
GitHub Copilot
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill ai-augmented-economist -a github-copilot -y
OpenCode
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill ai-augmented-economist -a opencode -y
Windsurf
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill ai-augmented-economist -a windsurf -y
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
## 🔥 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 +…
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Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy +…
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