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Interview Me

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pedrohcgs
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`).

Overview

Publisherpedrohcgs
Repositoryclaude-code-my-workflow
Skill nameinterview-me
Stars
1.6K
Forks
3.1K
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by pedrohcgs on GitHub. Read the source before you install it.

Installation

Install the Interview Me AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/pedrohcgs/claude-code-my-workflow/tree/main/.claude/skills/interview-me
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/pedrohcgs/claude-code-my-workflow.git /tmp/claude-code-my-workflow
mkdir -p .claude/skills
cp -r /tmp/claude-code-my-workflow/.claude/skills/interview-me .claude/skills/interview-me
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Interview Me in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Interview Me on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Interview Me is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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:

markdown
# 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.

Frequently asked questions

What does the Interview Me AI skill do?

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`).

Why use Interview Me on TypingMind?

Because you install it once and use it with any model. Interview Me is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Interview Me in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/pedrohcgs/claude-code-my-workflow/tree/main/.claude/skills/interview-me. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Interview Me?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Interview Me?

As many as you like. As long as a model supports skills, you can use Interview Me with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Interview Me AI skill free?

Yes. It is published on GitHub by pedrohcgs under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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