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interview-me

Interactive interview that formalizes a fuzzy research idea into a structured spec (RQ, hypotheses, identification, data needs, empirical strategy). Use when user says "interview me", "help me think through this idea", "I have a half-baked idea", "formalize this into a project", "walk me through framing a study". Multi-turn Q&A; saves spec to disk. NOT for lit review (`/lit-review`) or ideation from scratch (`/research-ideation`).

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Research Interview

Conduct a structured interview to help formalize 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. 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.


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?"
  • After the user answers, optionally ask: "Do you have a sense of what kind of paper this would be — reduced-form / structural / theory+empirics / descriptive / formal-theory / survey-experiment / unsure?" (See .claude/agents/methods-referee.md for the type definitions and .claude/references/discipline-cards.md for field-default frequencies.) Record the answer in the saved spec under the **Paper type:** header field; "unsure" is fine and is recorded as **Paper type:** unsure.

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?"

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:** [from conversation context]
**Paper type:** [reduced-form | structural | theory+empirics | descriptive | formal-theory | survey-experiment | unsure]

## 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., regression discontinuity around an eligibility cutoff]
- **Treatment:** [What varies]
- **Control:** [Comparison group]
- **Key identifying assumption:** [What must hold]
- **Robustness checks:** [Placebo tests, bandwidth sensitivity, etc.]

## 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: quality_reports/specs/research_spec_[sanitized_topic].md — the directory /grant-proposal, /data-management-plan, /power-analysis, and /preregister read from.


Post-Flight Verification (mandatory, CoVe — applies when the spec cites prior work)

The research spec's Motivation and Contribution sections typically reference prior papers by author + year. Those citations are hallucination-prone. Before saving the spec, run the Post-Flight Verification protocol from .claude/rules/post-flight-verification.md if the spec contains any citations.

Steps (skip if the spec cites zero papers)

  1. Extract claims: every paper-citation in the Motivation / Contribution sections ("Smith 2019 shows X"), any dataset-structure claims ("the CPS has field educ_attain"), any negative-literature assertions ("nobody has studied Y").
  2. Generate verification questions: specific, answerable questions per claim. "Does Smith (2019, JEL) Section 3 report finding X? Is the venue correct?"
  3. Spawn claim-verifier via the Agent tool with subagent_type=claim-verifier, in a fresh context — a named Agent call, not a conversation fork, which would inherit the draft. Hand it the claims + questions + source pointers (DOIs, arXiv links, master_supporting_docs/ PDFs if the user provided any during the interview). Do NOT include the drafted spec.
  4. Reconcile: PASS → attach green block to the spec. PARTIAL → mark unverifiable citations with uncertainty flags. FAIL → rewrite the affected paragraph using the verifier's evidence before saving the spec.

Skip conditions

  • Spec contains zero paper citations (pure-methodology specs with no lit references).
  • --no-verify flag.
  • The user explicitly said during the interview "I'll verify the literature myself."

Decision records (when tradeoffs surface)

If during the interview the researcher explicitly chose among alternatives — identification strategy (DiD vs IV vs RDD), data source (admin vs survey), outcome measure, sample scope, etc. — also write an ADR-style decision record for each choice. Use templates/decision-record.md and save to quality_reports/decisions/YYYY-MM-DD_[short-topic].md. Required fields: Status / Problem / Options considered / Decision + rationale / Consequences / Rejected alternatives.

Skip the ADR if the interview produced a single uncontested direction — ADRs are for decisions with live alternatives, not for announcing the default path.


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 skeptic 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.

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同一仓库的技能

Turn an incoming set of findings — from an AI reviewer, a referee report, a code review, a linter, or a second model — into verified fixes, without letting a confident misread damage correct work. Every finding is a CANDIDATE until checked against the actual source. Use whenever you receive review comments, audit findings, or a critique you did not write yourself, especially when the reviewer is a model or when the volume is too large to check by feel.

中文简介正在准备中。当前显示原文说明。

pedrohcgs/claude-code-my-workflow16642026年9月28日 更新

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中文简介正在准备中。当前显示原文说明。

pedrohcgs/claude-code-my-workflow16642026年9月28日 更新

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中文简介正在准备中。当前显示原文说明。

pedrohcgs/claude-code-my-workflow16642026年9月28日 更新

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中文简介正在准备中。当前显示原文说明。

pedrohcgs/claude-code-my-workflow16642026年9月28日 更新

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中文简介正在准备中。当前显示原文说明。

pedrohcgs/claude-code-my-workflow16642026年9月28日 更新

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中文简介正在准备中。当前显示原文说明。

pedrohcgs/claude-code-my-workflow16642026年9月28日 更新

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