deep-interview

Asks smart questions to help you think through a vague idea until it becomes clear.

Installation
Run `npx skills add "https://github.com/zereight/gitlab-mcp" --skill "deep-interview"` to install this skill, then follow its SKILL.md instructions for my next request.

Paste this into Claude Code, Cursor, or any agent that can run commands.

SKILL.mdShow the author's original SKILL.md
---
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

## 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. **Ask ONE question at a time** with current ambiguity context
3. **Score ambiguity** across dimensions:
   - 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
- Round 6+: **Simplifier** - probe for complexity removal
- Round 8+: **Ontologist** - find the essence (if ambiguity > 30%)

### Phase 4: Crystallize Spec
When ambiguity <= threshold, generate spec to `.omc/specs/deep-interview-{slug}.md`:
- Goal, Constraints, Non-Goals, Acceptance Criteria
- Assumptions Exposed & Resolved
- Ontology (Key Entities) with convergence tracking
- Interview Transcript

### Phase 5: Execution Bridge
Present options:
1. **Ralplan → OMG Autopilot** (Recommended): consensus-refine then execute
2. **Execute with omg-autopilot** (skip ralplan)
3. **Execute with ralph**: persistence loop
4. **Execute with team**: parallel agents
5. **Refine further**: continue interviewing

## Rules
- Ask ONE question at a time
- 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

Mirrored from the author's public source. Install counts from the open skills registry.

The systems behind these skills get built for partners every week.

Partner with us