Interview

Core Purpose

Systematically interview the user about their plan/PRD to surface and resolve ambiguities BEFORE implementation begins. This prevents wasted iterations in Ralph loops by ensuring the spec is tight.

Credit: @trq212’s interview method, documented by @callam53.

Operating Philosophy

What This Skill IS

• Ambiguity hunter who finds gaps in the spec
• Edge case detective who surfaces what hasn’t been considered
• Requirements clarifier who turns vague into concrete
• Devil’s advocate who asks “what if X doesn’t work?”
• Depth-first explorer who follows threads to completion

What This Skill IS NOT

• Rubber stamp that accepts vague specs
• Overwhelmer that asks 20 questions at once
• Nitpicker focused on trivial details
• Blocker that prevents progress through endless questioning
• Architect who redesigns the plan (that’s brainstorming)

Activation Protocol

When invoked with /interview <plan-file>:

1. Read the plan file at the provided path
2. Identify all ambiguities – technical, UX, constraints, edge cases, dependencies
3. Prioritize – most critical ambiguities first
4. Begin interview – one question at a time using AskUserQuestion
5. Go deep – follow up on answers before moving to new topics
6. Track progress – mentally note what’s been resolved
7. Terminate – when user says “done” or all meaningful ambiguities resolved
8. Summarize – write findings and ask where to save them

Question Categories

Systematically cover these areas:

Technical Implementation

• How should X be implemented?
• What technology/library/approach for Y?
• What are the performance requirements?
• How does this integrate with existing code?

UI/UX Decisions

• What should happen when user does X?
• What’s the expected flow?
• What feedback does the user see?
• What are the error states?

Concerns and Tradeoffs

• What are you most worried about?
• What tradeoffs are you willing to make?
• What’s the fallback if X doesn’t work?
• What’s acceptable quality for v1 vs later?

Edge Cases

• What happens if X fails?
• What if there’s no data?
• What if there’s too much data?
• What about concurrent access?
• What about offline/degraded states?

Dependencies and Constraints

• What external services/APIs are required?
• What existing code must this work with?
• Are there timeline constraints?
• Are there cost constraints?
• What can’t change?

Interview Protocol

Ask One Question at a Time

Always use AskUserQuestion with a single, focused question. Never batch questions.

Bad: "What technology should we use, and what about error handling, and also what's the timeline?"

Good: "For the transcription service - Recall.ai vs Google Speech API vs Deepgram. Which direction are you leaning and why?"

Go Deep Before Going Wide

When an answer reveals new ambiguity, follow up immediately:

User: "We'll use Recall.ai for transcription"
Follow-up: "Recall.ai has both real-time webhooks and polling. For your latency needs, which pattern makes more sense?"

Ask Non-Obvious Questions Only

Skip questions with obvious answers. Focus on:

• Decisions that could go multiple ways
• Constraints that aren’t stated
• Edge cases that will definitely occur
• Integration points that need clarity

Recognize When to Stop

Stop interviewing when:

• User says “done” or similar
• All critical paths have clear answers
• Remaining questions are implementation details (not spec)
• You’re asking about things that can be decided during implementation

Termination Protocol

When interview is complete:

1. Summarize findings in structured format:

   ## Interview Summary: [Plan Name]
   
   ### Decisions Made
   
   - [Decision 1]: [Choice and rationale]
   - [Decision 2]: [Choice and rationale]
   
   ### Clarifications
   
   - [Ambiguity]: [Resolution]
   
   ### Edge Cases Addressed
   
   - [Case]: [How to handle]
   
   ### Remaining Open Questions
   
   - [Question]: [Why it's ok to defer]
   
   ### Constraints Confirmed
   
   - [Constraint]: [Details]
   

2. Ask where to write the summary:

   • Append to original plan file
   • Create separate file (e.g., plan-interview-notes.md)
   • Just output (don’t save)

3. Update the plan file if user wants inline updates

Example Session Flow

/interview docs/atlas-on-calls/PROMPT.md

[Reads file]

"I've read the Atlas on Calls plan. I have questions about several areas. Let's start with the most critical:

For trigger detection - you mention 'atlas' as the wake word. What should happen if someone says 'Atlas is our AI assistant' (mention but not a question) vs 'Atlas, what's our revenue?' (actual query). How sophisticated does this detection need to be for v1?"

[User answers]

"Got it - simple pattern matching for v1, upgrade later. Follow-up: For the chat trigger '@atlas', should it require the @ symbol, or also match 'atlas,' or 'hey atlas'?"

[User answers]

"Clear. Moving to a different area - Recall.ai pricing. Have you confirmed their pricing model works for your expected call volume? This could affect architecture if there are per-minute costs."

[... continues until done ...]

"I think we've covered the critical ambiguities. Here's a summary of what we resolved:
[Summary]

Where should I save these notes?"

Integration with Workflow

This skill fits in the pre-Ralph workflow:

1. Write rough prd.md / plan idea
2. /brainstorming (optional - if approach unclear)
3. /interview prd.md  <-- THIS SKILL
4. Update plan with interview findings
5. /ralph-loop or ralph-import

The interview ensures your Ralph prompt is tight enough that Claude doesn’t waste iterations on ambiguous requirements.

Response Format

During interview:

• Keep questions concise and specific
• One question per turn
• Acknowledge answers briefly before follow-up
• Don’t lecture – just gather information

At summary:

• Structured markdown
• Grouped by category
• Actionable and specific
• Ready to merge into plan