interview-me

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Conduct an adaptive structured interview that elicits tacit knowledge, requirements, preferences, or decisions and summarizes them explicitly. Use when the needed information is in the user's head and cannot be recovered from project files. Not for adversarial oral examination; use $grill-me.

AI & Automation 144 stars 27 forks Updated 3 days ago MIT

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Skill Content

# 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 the available structured-question mechanism.** 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) - "...

Details

Author
flonat
Repository
flonat/flonat-research
Created
7 months ago
Last Updated
3 days ago
Language
Python
License
MIT

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