deep-interview

name	deep-interview
description	Socratic deep interview with mathematical ambiguity gating. Activate when user says: deep interview, interview me, ask me everything, don't assume, make sure you understand, ouroboros, socratic, I have a vague idea, not sure exactly what I want.
argument-hint	[--quick|--standard|--deep] <idea or vague description>

Deep Interview

Ouroboros-inspired Socratic questioning with mathematical ambiguity scoring. Replaces vague ideas with crystal-clear specifications by asking targeted questions that expose hidden assumptions.

Pipeline

deep-interview → ralplan (consensus refinement) → omg-autopilot (execution)

When to Use

• User has a vague idea and wants thorough requirements gathering
• Task is complex enough that jumping to code would waste cycles
• User wants mathematically-validated clarity before execution

When NOT to Use

• Detailed specific request with file paths → execute directly
• Quick fix → delegate to @executor or /ralph
• User says "just do it" → respect their intent

Interactive Hook Protocol

MANDATORY: Use vscode_askQuestions for ALL user-facing questions in this skill (when available). If vscode_askQuestions is NOT available (e.g., Copilot CLI), present numbered options in markdown and ask the user to respond with a number or freeform text. This ensures structured input collection with selectable options, consistent UX, and clear decision tracking.

When to Fire Hooks

Trigger Point	Question Type	Options Required
Phase 2 each round	Ambiguity-targeted question	3-5 options + freeform
Phase 3 challenges	Assumption validation	Yes/No + "It depends..."
Phase 4 spec review	Confirm crystallized spec	Approve / Revise / Add constraints
Phase 5 execution bridge	Choose next workflow	5 predefined options

Hook Format Rules

• header: Short unique ID, e.g. "interview-round-3", "spec-approval"
• question: The Socratic question targeting the weakest clarity dimension
• options: Provide 3-5 selectable answers that represent likely user intents
  • First option: most common/expected answer (mark as recommended)
  • Last option: always include an "Other / Let me explain..." escape hatch
• allowFreeformInput: Always true — user can override any option with their own words
• After receiving the answer, score ambiguity immediately and report progress

Example Hook Call

vscode_askQuestions([{
  header: "goal-scope",
  question: "What's the primary output you expect? (Ambiguity: 72% → targeting Goal Clarity)",
  options: [
    { label: "A working CLI tool", recommended: true },
    { label: "A library/SDK for other developers" },
    { label: "A web application with UI" },
    { label: "A background service/daemon" },
    { label: "Other / Let me explain..." }
  ],
  allowFreeformInput: true
}])

Phases

Phase 1: Initialize

1. Parse the user's idea
2. Detect brownfield vs greenfield (use @explore to check codebase)
3. For brownfield: map relevant codebase areas
4. Initialize ambiguity score at 100%

Phase 2: Interview Loop

Repeat until ambiguity <= 20% or user exits early:

1. Generate question targeting the WEAKEST clarity dimension
2. HOOK: Ask ONE question via vscode_askQuestions with:
   • Current ambiguity score in the question text
   • 3-5 contextual options derived from codebase analysis and prior answers
   • allowFreeformInput: true always
3. Score ambiguity across dimensions after receiving answer:
   • Goal Clarity (40% weight for greenfield, 35% brownfield)
   • Constraint Clarity (30% / 25%)
   • Success Criteria (30% / 25%)
   • Context Clarity (N/A / 15% for brownfield)
4. Report progress with dimension scores and gaps
5. Track ontology (key entities, stability ratio)

Phase 3: Challenge Agents

• Round 4+: Contrarian - challenge core assumptions
  • HOOK: Present assumption + counter-argument, ask user to confirm/revise via vscode_askQuestions
• Round 6+: Simplifier - probe for complexity removal
  • HOOK: Present simplification options, ask user which can be dropped
• Round 8+: Ontologist - find the essence (if ambiguity > 30%)

Phase 4: Crystallize Spec

When ambiguity <= threshold, generate spec to .omg/specs/deep-interview-{slug}.md:

• Goal, Constraints, Non-Goals, Acceptance Criteria
• Assumptions Exposed & Resolved
• Ontology (Key Entities) with convergence tracking
• Interview Transcript
• HOOK: Spec approval — present spec summary via vscode_askQuestions:

  header: "spec-approval"
  question: "Review the crystallized spec. Ready to proceed?"
  options: [Approve & continue, Revise specific sections, Add more constraints, Restart interview]
  

Phase 5: Execution Bridge

HOOK: Present execution options via vscode_askQuestions:

header: "execution-bridge"
question: "Spec is complete (ambiguity: X%). Choose execution path:"
options: [
  { label: "Ralplan → OMG Autopilot", description: "Consensus-refine then execute", recommended: true },
  { label: "Execute with omg-autopilot", description: "Skip ralplan, direct execution" },
  { label: "Execute with ralph", description: "Persistence loop" },
  { label: "Execute with team", description: "Parallel agents" },
  { label: "Refine further", description: "Continue interviewing" }
]

Rules

• Ask ONE question at a time
• Use vscode_askQuestions for user-facing questions when available; in CLI, present numbered markdown options
• Target the WEAKEST clarity dimension each round
• Gather codebase facts via @explore BEFORE asking user
• Score ambiguity after every answer
• Do not proceed until ambiguity <= threshold (default 20%)
• Hard cap at 20 rounds, soft warning at 10
• Include ambiguity % in every question's text so user sees progress