/interview-me

name: interview-me
description: Extracts what the user actually wants instead of what they think they should want. Achieves this through one-question-at-a-time interview until ~95% confidence about the underlying intent. Use when an ask is underspecified ("build me X" without "for whom" or "why now"), when the user explicitly invokes ("interview me", "grill me", "are we sure?", "stress-test my thinking"), or when you catch yourself silently filling in ambiguous requirements before any plan, spec, or code exists.

Interview Me

Overview

What people ask for and what they actually want are different things. They ask for "a dashboard" because that's what one asks for, not because a dashboard solves their problem. They say "make it faster" without a number to hit.

The cheapest moment to find this gap is before any plan, spec, or code exists. Once you've started building, switching costs are real, and the user will rationalize the wrong thing into a "good enough" thing. The misfit gets locked in.

This skill closes the gap before it costs anything. The other Define-phase skills assume you already know roughly what you want: `idea-refine` generates variations from an idea, `spec-driven-development` writes the requirements down, `doubt-driven-development` stress-tests a plan after you've drafted one. Interview-me is the part before all of those, where you ask one question at a time, with your best guess attached, until you can predict what the user is going to say before they say it.

When to Use

Apply this skill when:

• The ask is missing at least one of: **who** the user is, **why** they want it, what **success** looks like, what the binding **constraint** is
• The request is conventional rather than specific ("build me X", "make it faster") and you can't unpack the convention without guessing
• You're tempted to start with assumptions you haven't surfaced
• The user hasn't said which value they're optimizing for when two reasonable ones are in tension (simplicity vs. flexibility, cost vs. speed)
• The user explicitly invokes: "interview me", "grill me", "before we start, are we sure?", "stress-test my thinking"

**When NOT to use:**

• The ask is unambiguous and self-contained ("rename this variable", "fix this typo")
• The user has explicitly asked for speed over verification
• Pure information requests ("how does X work?", "what does this code do?")
• Mechanical operations (renames, formats, file moves)
• You already have ≥95% confidence; re-read the stop condition below before assuming you don't

Loading Constraints

This skill needs a live, responsive user. **Do not invoke in non-interactive contexts** like CI pipelines, scheduled runs, `/loop`, or autonomous-loop. If you're in one of those and the ask is underspecified, flag that as a blocker for the user instead of guessing.

The Process

Step 1: Hypothesize, with a confidence number

Before asking anything, write down your current best read of what the user wants in **one sentence**, plus an honest confidence number (0–100%):

HYPOTHESIS: You want a way to answer "how are we doing?" in standup, and "dashboard" was the convention that came to mind.
CONFIDENCE: ~30% — missing: who it's for, what "metrics" means in context, and what success looks like

The number forces honesty. If you wrote down a high number but can't actually predict the user's reactions to the next three questions you'd ask, the number is wrong. Start at the confidence level you can defend.

When confidence is below ~70%, append a brief reason on the same line — what's still unresolved or missing. This tells the user exactly what the interview needs to surface, and prevents the number from being a vague signal.

Step 2: Ask one question at a time, each with a guess attached

Format:

Q: <one focused question>
GUESS: <your hypothesis for the answer, with the reasoning that produced it>

Wait for the user to react before asking the next question.

**Why one at a time, not a batch:**

• The user can't react to your hypotheses if you bury them in a list
• Batches encourage skim-reading and surface answers
• The third question often depends on the answer to the first; asking them all at once locks in the wrong framing
• The user's energy for thinking carefully is finite; spend it one question at a time

**Why attach a guess:**

• The user reacts faster to a wrong guess than they generate an answer from scratch
• It commits you to a hypothesis you can be visibly wrong about, which keeps you honest
• It surfaces *your* assumptions, which is what the interview is meant to expose

The risk here is a polite user agreeing with your guess to be agreeable. Mitigate by being visibly willing to be wrong, and occasionally guess in a direction you expect the user to push back on.

Step 3: Listen for "want vs. should want"

The most dangerous answers are the ones where the user says what a thoughtful answer *sounds like* rather than what they actually want. Watch for:

• Answers that pattern-match best-practice talk ("I want it to be scalable", "clean architecture") without specifics
• Answers that defer to convention ("the way most apps do it", "the standard approach")
• Phrases like "I should probably…", "I think I'm supposed to…", "good engineering practice says…"
• Buzzwords as goals — when "modern", "scalable", "robust" are the answer instead of a specific outcome

When you hear these, the question to ask is:

> *"If you didn't have to justify this to anyone, what would you actually want?"*

That single question often does more work than the previous five.

Step 4: Restate intent in the user's own words

When your confidence is high, write back what you now think the user wants. Keep it tight (5–8 lines), use their language where possible, and structure it so the user can confirm or correct line by line:

Here's what I now think you want:

- Outcome:      <one line>
- User:         <one line — who benefits>
- Why now: