What problem does it solve?
Deep Interview implements Ouroboros-inspired Socratic questioning with mathematical ambiguity scoring. It replaces vague ideas with crystal-clear specifications by asking targeted questions that expose hidden assumptions, measuring clarity across weighted dimensions, and refusing to proceed until ambiguity drops below a configurable threshold (default: 20%). The output feeds into a 3-stage pipeline: deep-interview → ralplan (consensus refinement) → autopilot (execution), ensuring maximum clarity at every stage.
Core Features & Use Cases
- One-question-at-a-time Socratic interviewing to surface hidden assumptions and progressively clarify requirements.
- Mathematical ambiguity scoring with explicit thresholds and weak-dimension targeting to guide the interview.
- Brownfield and greenfield context handling, with optional codebase exploration and ontology tracking to stabilize concepts.
- Multi-stage execution bridge (deep-interview → omc-plan → autopilot) with state persistence and transcript/documentation of decisions.
Quick Start
Provide your initial idea and I will start one-question-at-a-time Socratic questioning until ambiguity is <= 20%, then generate and save the spec.