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deep-interview

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.

Quellinformationen

Repository
zereight/gitlab-mcp
Letzte Quellaktivität
12. April 2026 um 05:11
Erkannte Sprache von SKILL.md
Englisch
Sterne
2.079
Forks
364

Installationsoptionen

Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.

Quelldateien prüfen

Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.

SKILL.md wird angezeigt

SKILL.md
Quellanweisungen · Schreibgeschützte Vorschau
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
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