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/interview-me

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

From plugin
auto-empirical-research-skills
3.8k200 skills
Install
$ npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill interview-me --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/interview-me

Context 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

SKILL.md

interview-me.SKILL.md
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"]

Research Interview

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.

---

How This Works

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.

---

Interview Structure

Phase 1: The Big Picture (1-2 questions)

  • "What phenomenon or puzzle are you trying to understand?"
  • "Why does this matter? Who should care about the answer?"

Phase 2: Theoretical Motivation (1-2 questions)

  • "What's your intuition for why X happens / what drives Y?"
  • "What would standard theory predict? Do you expect something different?"

Phase 3: Data and Setting (1-2 questions)

  • "What data do you have access to, or what data would you ideally want?"
  • "Is there a specific context, time period, or institutional setting you're focused on?"

For this project, also probe:

  • Can this be answered with the existing EDM + Land Registry + Zoopla data?
  • Does this require new data (e.g. water company financials, bathing water quality, health data)?

Phase 4: Identification (1-2 questions)

  • "Is there a natural experiment, policy change, or source of variation you can exploit?"
  • "What's the biggest threat to a causal interpretation?"

Phase 5: Expected Results (1-2 questions)

  • "What would you expect to find? What would surprise you?"
  • "What would the results imply for policy or theory?"

Phase 6: Contribution (1 question)

  • "How does this differ from what's already been done? What's the gap you're filling?"

---

After the Interview

Once enough information is gathered (typically 5-8 exchanges), produce:

Research Specification Document

# 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`.

---

Interview Style

  • **Be curious, not prescriptive.** Draw out the researcher's thinking, don't impose ideas.
  • **Probe weak spots gently.** "What would a sceptic say about...?" rather than "This won't work."
  • **Build on answers.** Each question should follow from the previous response.
  • **Know when to stop.** If the researcher has a clear vision after 4-5 exchanges, move to the specification.
  • **Project-aware.** Connect ideas to the existing sewage project infrastructure where relevant.
Read more
Ships withauto-empirical-research-skills

📌 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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