interview-me
interview-me is an productivity AI skill with a core value of 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. It
helps developers solve real-world problems in the productivity domain, boosting
efficiency, automating repetitive tasks, and optimizing workflows.
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.
Quick Facts
mkdir -p ./skills/interview-me && curl -sfL https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/skills/interview-me/SKILL.md -o ./skills/interview-me/SKILL.md Run in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).
Skill Content
# 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 likeThe 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
🎯 Best For
- Claude users
- Knowledge workers
- Remote teams
- Professionals
💡 Use Cases
- Using interview-me in daily workflow
- Automating repetitive productivity tasks
📖 How to Use This Skill
- 1
Install the Skill
Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.
- 2
Load into Your AI Assistant
Open Claude and reference the skill. Paste the SKILL.md content or use the system prompt tab.
- 3
Apply interview-me to Your Work
Provide context for your task — paste source material, describe your audience, or share existing work to guide the AI.
- 4
Review and Refine
Edit the AI output for accuracy, tone, and completeness. Add human insight where the AI lacks context.
❓ Frequently Asked Questions
How do I install interview-me?
Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/interview-me/SKILL.md, ready to use.
Can I customize this skill for my team?
Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.
⚠️ Common Mistakes to Avoid
Not reading the full skill
Skills contain important context and edge cases beyond the quick start.