jmstar85/oh-my-githubcopilot/deep-interview

Claude Code and agent skill by jmstar85/oh-my-githubcopilot

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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.

Install

npx vskill@latest install jmstar85/oh-my-githubcopilot/deep-interview

SKILL.md

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 PointQuestion TypeOptions Required
Phase 2 each roundAmbiguity-targeted question3-5 options + freeform
Phase 3 challengesAssumption validationYes/No + "It depends..."
Phase 4 spec reviewConfirm crystallized specApprove / Revise / Add constraints
Phase 5 execution bridgeChoose next workflow5 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
  1. 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)
  1. Report progress with dimension scores and gaps
  2. 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

Security

  • Security scanPassed, 100 of 100Pattern checks on the skill's files.
  • Model reviewNot runA model review is scheduled for the next rescan.

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

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