interview-me

name	interview-me
description	Use when you need to conduct a structured interview to extract knowledge or preferences.
disable-model-invocation	true
argument-hint	[brief topic or 'start fresh']
allowed-tools	Read, Write, Edit

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 open-ended exploration.

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How This Works

This is a conversational skill. Instead of producing a report immediately, you conduct an interview by asking questions one at a time, probing deeper based on answers, and building toward a structured research specification.

Do NOT use AskUserQuestion. Ask questions directly in your text responses, one or two at a time. Wait for the user to respond before continuing.

Before starting, read .context/profile.md and .context/projects/_index.md to understand the researcher's areas and active projects. If the topic relates to an existing project, read its context file too.

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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?"

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?"

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Adapting to the Research Area

the user's work spans multiple disciplines. Adapt the interview to the domain:

• Human-AI collaboration / MCDM: Focus on decision architecture, experimental design, behavioural measures, and what "better" decisions look like.
• Multi-agent systems: Focus on agent design, interaction protocols, equilibrium concepts, and simulation methodology.
• Organisational behaviour: Focus on mechanisms, field vs. lab settings, mediators/moderators, and internal validity.
• Carbon markets / environmental: Focus on policy variation, compliance data, market microstructure, and welfare implications.

If the research is non-quantitative (conceptual, design science, qualitative), adjust: replace "Identification" with "Analytical Framework" and "Data" with "Empirical/Evidence Strategy".

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Phase 7: Field Calibration (optional, auto-triggered)

Auto-triggers when: the project has no .context/field-calibration.md, or it exists but still contains <placeholders>.

Skip when: the file already exists with populated content, unless the user explicitly asks to update it.

Ask 2–3 targeted questions:

• "Which journals or conferences are you targeting? I can cross-reference venue rankings." (Use .context/resources/venue-rankings.md to validate and suggest alternatives.)
• "Which seminal papers would a reviewer in this subfield expect to see cited?"
• "What's the typical identification strategy in this subfield — and what do reviewers most often attack?"

After the interview, populate .context/field-calibration.md from answers combined with Research Spec content. Use the template at skills/init-project-research/templates/field-calibration.md.

If field-calibration already exists with content: ask the user whether to update specific sections or keep as-is.

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After the Interview

Once you have enough information (typically 5–8 exchanges), produce a Research Specification Document:

# Research Specification: [Title]

**Date:** [YYYY-MM-DD]
**Researcher:** the user

## 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., DiD, experiment, simulation, case study]
- **Treatment/Manipulation:** [What varies]
- **Control/Comparison:** [Comparison group or baseline]
- **Key identifying assumption:** [What must hold]
- **Robustness checks:** [Pre-trends, placebo, alternative specifications]

## Data

- **Primary dataset:** [Name, source, coverage]
- **Key variables:** [Treatment, outcome, controls]
- **Sample:** [Unit of observation, time period, N]

## 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]

Save to: the project root or docs/ if inside a research project, or present to the user for placement.

Also produces (if Phase 7 triggered): .context/field-calibration.md — the per-project domain profile that agents use to calibrate reviews.

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Interview Style

• Be curious, not prescriptive. Your job is to draw out the researcher's thinking, not impose your own ideas.
• Probe weak spots gently. If the identification strategy sounds fragile, ask "What would a sceptic say about...?" rather than "This won't work because..."
• 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. Don't over-interview.
• Use British English throughout (the user's preference).

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Cross-References

Skill	When to use instead/alongside
/devils-advocate	After the spec is written — stress-test the idea
/literature	To find related work mentioned during the interview
/init-project-research	To scaffold a project once the spec is approved (seeds empty field-calibration)