zereight/gitlab-mcp

deep-interview

Socratic deep interview with mathematical ambiguity gating.

Zobacz źródło

Jak używać

  1. Skopiuj polecenie instalacji i sprawdź źródła przed instalacją we własnym środowisku agenta.
  2. W oryginale sprawdź wymagane narzędzia, dane dostępowe i obsługiwane środowiska.
  3. Opisz dane wejściowe i oczekiwany rezultat; porównaj wynik ze źródłami.
npx skills add https://github.com/zereight/gitlab-mcp --skill deep-interview

Przed rozpoczęciem

  • Instalacja nie obejmuje subskrypcji modelu, limitu API ani uprawnień do plików.
  • Gwiazdki i instalacje pokazują aktywność, a nie niezależny test jakości lub bezpieczeństwa.
Oryginalny dokument Skill

Treść z repozytorium z zachowaniem nagłówków, przykładów, kodu, tabel, linków i obrazów.

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)
  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
  • 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
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