# Interview Me

> Use when you need to conduct a structured interview to extract knowledge or preferences.

- **Type:** Skill
- **Install:** `agentstack add skill-flonat-claude-research-interview-me`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [flonat](https://agentstack.voostack.com/s/flonat)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [flonat](https://github.com/flonat)
- **Source:** https://github.com/flonat/claude-research/tree/main/skills/interview-me

## Install

```sh
agentstack add skill-flonat-claude-research-interview-me
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# 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.

---

## 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.

---

## 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?"

---

## 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".

---

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

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

**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.

---

## After the Interview

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

```markdown
# 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.

---

## 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).

---

## 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) |

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [flonat](https://github.com/flonat)
- **Source:** [flonat/claude-research](https://github.com/flonat/claude-research)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-flonat-claude-research-interview-me
- Seller: https://agentstack.voostack.com/s/flonat
- Browse the marketplace: https://agentstack.voostack.com/browse

---
Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.