Como usar
- Copie o comando e verifique o pacote antes de instalar no seu ambiente de agente.
- Consulte no original as ferramentas, credenciais e ambientes necessários.
- Descreva os dados de entrada e a entrega esperada; confira a saída com as fontes.
npx skills add https://github.com/zereight/gitlab-mcp --skill deep-interviewAntes de começar
- A instalação não inclui assinatura de modelo, franquia API ou acesso aos seus arquivos.
- Estrelas e instalações indicam atividade, não um teste independente de qualidade ou segurança.
Documento original do Skill
Renderizado do repositório de origem, preservando títulos, exemplos, código, tabelas, links e imagens.
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
- Parse the user's idea
- Detect brownfield vs greenfield (use @explore to check codebase)
- For brownfield: map relevant codebase areas
- Initialize ambiguity score at 100%
Phase 2: Interview Loop
Repeat until ambiguity <= 20% or user exits early:
- Generate question targeting the WEAKEST clarity dimension
- Ask ONE question at a time with current ambiguity context
- 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)
- Report progress with dimension scores and gaps
- 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:
- Ralplan → OMG Autopilot (Recommended): consensus-refine then execute
- Execute with omg-autopilot (skip ralplan)
- Execute with ralph: persistence loop
- Execute with team: parallel agents
- 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
