Interview Me

Interview Me

Cherry-picked from addyosmani/agent-skills (MIT) 2026-05-26.

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. It complements (does not replace) batched-options elicitation skills: this skill is for free-form interview when you don't yet know enough to offer a bounded set of choices. Use the batched-options skill when the user knows roughly what they want but needs to pick between framings; use this skill when you can't write a one-sentence statement of intent yet.

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, or autonomous loops. 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:      <one line — what changed>
- Success:      <one line — how we know it worked>
- Constraint:   <one line — the binding limit>
- Out of scope: <one line — what we're explicitly not doing>

Yes / no / refine?

Including "Out of scope" is non-negotiable. Half of misalignment is silent disagreement about what is not being built.

Step 5: Confirm — explicit yes, not "whatever you think"

The gate is an explicit "yes." The following are not yes:

• "Whatever you think is best." → The user is delegating, which means they don't have 95% confidence either. Re-ask with two concrete options framed as a choice.
• "Sounds good." → Ambiguous. Ask: "Anything you'd refine?" Silence isn't confirmation.
• "Sure, let's go." → Often a polite exit, not an endorsement. Same follow-up.
• Silence followed by "okay let's start." → The user has given up on the interview, not converged. Stop and ask whether you've missed something.

If they correct you, fold the correction in and restate. Loop until you get an explicit yes.

The 95% Confidence Stop

You're done when you can answer yes to this:

Can I predict the user's reaction to the next three questions I would ask?

If yes, you have shared understanding. Stop interviewing and produce the restate. If no, you're not done; ask the next question.

This is a checkable test, not a vibe. It also has a floor: if you've gone several rounds and still can't predict, that's information about the ask, not a reason to keep grinding. Stop and tell the user: "I've asked X questions and I still can't predict your reactions. Something foundational is missing. Want to step back?"

Output

The output of this skill is a confirmed statement of intent: the restate from Step 4, with an explicit yes from Step 5. That's the deliverable. Specs, plans, and task lists are downstream; they consume the intent this skill produces.

If the user wants the intent to persist (a multi-session project, a handoff to another collaborator), offer to save it to a project-local file (commonly docs/intent/[topic].md). Only save if they confirm.

Common Rationalizations

Red Flags

• Three or more questions in a single message: that's batching, not interviewing
• A question without your hypothesis attached: that's surveying, not committing
• Accepting "whatever you think is best" as a terminal answer
• Producing a spec, plan, or task list before the user has explicitly confirmed your restate
• Questions framed as "what would be best practice?" instead of "what do you actually want?"
• The user gives a sophistication-signaling answer ("scalable", "clean", "modern") and you accept it without probing whether it's what they actually want
• Three or more rounds without your confidence visibly rising: you're asking the wrong questions, step back and reframe
• A confidence number below ~70% with no reason attached: the user can't help close the gap if they don't know what's missing
• Saving the intent doc before the user has confirmed (the doc itself implies a yes the user didn't give)
• Skipping the "Out of scope" line in the restate (silent disagreement about non-goals is half of misalignment)

Verification

After applying interview-me:

• An explicit hypothesis with a confidence number was stated in the first turn
• Every confidence number below ~70% was accompanied by a one-line reason (what's still unresolved or missing)
• Questions were asked one at a time, each with the agent's guess attached
• At least one "what would you actually want if you didn't have to justify it?" probe ran when the user gave a sophistication-signaling or convention-signaling answer
• A concrete restate (Outcome / User / Why now / Success / Constraint / Out of scope) was written back to the user
• The user confirmed the restate with an explicit yes (not "whatever you think," not "sounds good," not silence)
• At the stop point, the agent could predict reactions to the next three questions it would ask

Attribution

This skill adapts content from addyosmani/agent-skills under MIT License. The hypothesis-with-confidence-number, predict-next-3-reactions stop test, hollow-yes rejection list, and "if you didn't have to justify this" probe trace to the upstream skill.