pipeline
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest +…
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
$ npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill interview-me --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/interview-meContext preview
The summary Claude sees to decide when to auto-load this skill.
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
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.
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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.
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For this project, also probe:
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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`.
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📌 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 |
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