interview-me – skill Lập trình cho agent AI

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 is a subjective estimate, not a calibrated probability or evidence of shared understanding. 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 offer a neutral question or alternative when a guess could anchor the answer. Never deliberately misstate your belief to provoke a reaction.

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 the concrete restatement

Accept clear contextual agreement such as “sounds good,” “sure, let’s go,” or “okay, start.” Honor “whatever you think is best” as delegation for routine choices: state the selected assumption and proceed within scope. Ask a focused follow-up only when a material ambiguity or required authorization remains. Silence is not approval. Do not infer disengagement, hidden disagreement or lack of confidence from a user's wording alone.

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 subjective stopping heuristic; user confirmation and sufficient task information matter more than mind-reading. 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 docs/intent/[topic].md. Only save if they confirm.

Example

A short before-and-after.

Without interview-me:

User:  build me a dashboard for our metrics
Agent: <starts proposing chart libraries and layouts>

The agent has assumed: who it's for, what metrics, what "for our team" means, what success looks like. None of these are stated. Every assumption is a place the user can later say "that's not what I meant."

With interview-me:

User: build me a dashboard for our metrics

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

Q:     When you say "how are we doing?", who's asking — you alone, the engineering team in standup, or up the chain (e.g., your manager, an exec, a board)?
GUESS: engineering team in standup, because "we" usually scopes that way and standups are where this question gets asked. If it's for execs the metrics and the framing change a lot.

User: It's actually for me. I keep losing track of which experiments are running and what their early signal looks like.

Agent:
Updated read: not a team dashboard at all. It's a personal experiment tracker. Confidence: ~60% — still missing: what "early signal" means and what done looks like.

Q:     Is the gap that you don't know which experiments exist, or that you can't see their results in one place?
GUESS

… (nội dung đầy đủ có trong gói tải về)