interview-me
Helps your agent understand what you want. It asks questions one at a time until the goal is clear, before any planning.
What it does
This tool helps understand what you truly want before starting a project. It asks questions one by one to uncover your real needs, rather than just what you superficially ask for.
When to use it
- ✓When you have a vague idea for a project.
- ✓If you ask for something generic like 'make it faster' without a clear goal.
- ✓Before starting to plan or code something complex.
Example of what to ask your AI
“Interview me about what I need for this new app.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The AI asks one question at a time, explaining what it thinks you want and why. This helps clarify your ideas before any work begins.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add sickn33/agentic-awesome-skills --skill interview-meOr download just the file:
curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/interview-me/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
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:
…
FAQ
Why does the AI ask one question at a time?
Asking one question at a time prevents you from getting lost in too much information and allows the AI to adjust its questions based on your answers, ensuring it understands exactly what you need.
What if I agree with the AI's guess, but it's wrong?
The AI is designed to be visibly willing to be wrong. If you agree but later realize it wasn't quite right, the AI can ask more questions to correct the course.
When should I NOT use this tool?
Don't use it if the request is simple and clear (like fixing a typo), if you need speed over verification, or if it's purely for general information requests.
Details
Original description by the author
Draw out what the user actually wants with one-question-at-a-time interviews until intent is clear — before any plan, spec, or code exists.
Popularity
- GitHub stars
- 48k
- Ranking position
- #3349
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
More skills from this repository
00-andruia-consultant
Helps your agent start or improve AI projects. It analyzes code, suggests tools, and creates action plans in Spanish.
sickn33/agentic-awesome-skills
007
Lets your agent perform security audits and threat modeling. It's useful for protecting projects against attacks and vulnerabilities.
sickn33/agentic-awesome-skills
10-andruia-skill-smith
Helps your agent create or improve AI tools. It defines how tools work, writes documentation, and validates the structure.
sickn33/agentic-awesome-skills
20-andruia-niche-intelligence
Lets your agent create a summary about a specific industry. It's useful for understanding the context, rules, usage patterns, and risks of a project, using reliable sources.
sickn33/agentic-awesome-skills
Similar skills
agent-architecture-audit
Makes your agent check if everything is working correctly. It finds and fixes problems in AI systems, ensuring they perform as expected.
affaan-m/ECC
agent-eval
Helps your agent compare different AI coding tools. It measures which one is faster, cheaper, and more reliable for specific tasks.
affaan-m/ECC
agent-harness-construction
Makes your agent better understand how to perform tasks. It optimizes the tools and instructions the agent uses to increase its chances of success.
affaan-m/ECC