pipeline
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy +…
Structured conversational interview to formalise a research idea or extension into a concrete specification with hypotheses and empirical strategy. This skill should be used when asked to "interview me", "help me think through an idea", "formalise this idea", or "start fresh" on
Claude Code
$ npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill interview-me --agent claude-code
Codex
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill interview-me -a codex -y
Cursor
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill interview-me -a cursor -y
Gemini CLI
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill interview-me -a gemini-cli -y
GitHub Copilot
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill interview-me -a github-copilot -y
OpenCode
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill interview-me -a opencode -y
Windsurf
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill interview-me -a windsurf -y
name: interview-me description: Structured conversational interview to formalise a research idea or extension into a concrete specification with hypotheses and empirical strategy. This skill should be used when asked to "interview me", "help me think through an idea", "formalise this idea", or "start fresh" on a new research direction. argument-hint: "[brief topic or 'start fresh']" allowed-tools: ["Read", "Write"]
Conduct a structured interview to help formalise a research idea into a concrete specification.
**Input:** `$ARGUMENTS` — a brief topic description or "start fresh" for an open-ended exploration.
---
This is a **conversational** skill. Ask questions one at a time, probe deeper based on answers, and build toward a structured research specification.
Ask questions directly in text responses, one or two at a time. Wait for the user to respond before continuing.
---
For this project, also probe:
---
Once enough information is gathered (typically 5-8 exchanges), produce:
# Research Specification: [Title] **Date:** YYYY-MM-DD ## Research Question [Clear, specific question in one sentence] ## Motivation [2-3 paragraphs: why this matters, theoretical context, policy relevance] ## Hypothesis [Testable prediction with expected direction] ## Empirical Strategy - **Method:** [e.g., Difference-in-Differences] - **Treatment:** [What varies] - **Control:** [Comparison group] - **Key identifying assumption:** [What must hold] - **Robustness checks:** [Pre-trends, placebo tests, etc.] ## Data - **Primary dataset:** [Name, source, coverage] - **Key variables:** [Treatment, outcome, controls] - **Sample:** [Unit of observation, time period, N] - **Available in project:** [Yes/No — what exists vs what's needed] ## Expected Results [What the researcher expects to find and why] ## Contribution [How this advances the literature — 2-3 sentences] ## Open Questions [Issues raised during the interview that need further thought] ## Feasibility Assessment - Data availability: [Ready / Partially available / Needs collection] - Infrastructure reuse: [What from the existing pipeline can be reused] - Estimated effort: [Low / Medium / High]
Save to `output/log/research_spec_[topic].md`.
---
## 🔥 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…