zereight/gitlab-mcp

deep-interview

Socratic deep interview with mathematical ambiguity gating.

Quelltext ansehen

Verwendung

  1. Installationsbefehl kopieren und das Quellpaket vor der Installation in der eigenen Agentenumgebung prüfen.
  2. Benötigte Werkzeuge, Zugangsdaten und Hosts im Original prüfen.
  3. Eingabe und gewünschtes Ergebnis beschreiben und die Ausgabe mit den Quellen abgleichen.
npx skills add https://github.com/zereight/gitlab-mcp --skill deep-interview

Vor dem Start

  • Die Installation umfasst weder Modellabo noch API-Kontingent oder Dateizugriffsrechte.
  • Sterne und Installationen zeigen Aktivität, keine unabhängige Qualitäts- oder Sicherheitsprüfung.
Originales Skill-Dokument

Aus dem Quell-Repository gerendert; Überschriften, Beispiele, Code, Tabellen, Links und Bilder bleiben erhalten.

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
aus demselben Repository

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