deep-interview

<Use_When>

• User has a vague idea and wants thorough requirements gathering before execution
• User says "deep interview", "interview me", "ask me everything", "don't assume", "make sure you understand"
• User says "ouroboros", "socratic", "I have a vague idea", "not sure exactly what I want"
• User wants to avoid "that's not what I meant" outcomes from autonomous execution
• Task is complex enough that jumping to code would waste cycles on scope discovery
• User wants mathematically-validated clarity before committing to execution </Use_When>

<Do_Not_Use_When>

• User has a detailed, specific request with file paths, function names, or acceptance criteria -- execute directly
• User wants to explore options or brainstorm -- use omc-plan skill instead
• User wants a quick fix or single change -- delegate to executor or ralph
• User says "just do it" or "skip the questions" -- respect their intent
• User already has a PRD or plan file -- use ralph or autopilot with that plan </Do_Not_Use_When>

<Why_This_Exists> AI can build anything. The hard part is knowing what to build. OMC's autopilot Phase 0 expands ideas into specs via analyst + architect, but this single-pass approach struggles with genuinely vague inputs. It asks "what do you want?" instead of "what are you assuming?" Deep Interview applies Socratic methodology to iteratively expose assumptions and mathematically gate readiness, ensuring the AI has genuine clarity before spending execution cycles.

Inspired by the Ouroboros project which demonstrated that specification quality is the primary bottleneck in AI-assisted development. </Why_This_Exists>

<Execution_Policy>

• Ask ONE question at a time -- never batch multiple questions
• Target the WEAKEST clarity dimension with each question
• Make weakest-dimension targeting explicit every round: name the weakest dimension, state its score/gap, and explain why the next question is aimed there
• Gather codebase facts via explore agent BEFORE asking the user about them
• For brownfield confirmation questions, cite the repo evidence that triggered the question (file path, symbol, or pattern) instead of asking the user to rediscover it
• Score ambiguity after every answer -- display the score transparently
• Keep prompt payloads budgeted: summarize or trim oversized initial context/history before composing question, scoring, spec, or handoff prompts
• If the user's initial context is oversized, create a concise prompt-safe summary first and wait for that summary before ambiguity scoring, question generation, or downstream execution handoff
• Do not proceed to execution until ambiguity ≤ the resolved threshold for this run
• Allow early exit with a clear warning if ambiguity is still high
• Persist interview state for resume across session interruptions
• Challenge agents activate at specific round thresholds to shift perspective </Execution_Policy>

<Autoresearch_Mode> When arguments include --autoresearch, Deep Interview becomes the zero-learning-curve setup lane for the stateful autoresearch skill.

• If no usable mission brief is present yet, start by asking: "What should autoresearch improve or prove for this repo?"
• After the mission is clear, collect an evaluator command. If the user leaves it blank, infer one only when repo evidence is strong; otherwise keep interviewing until an evaluator is explicit enough to launch safely.
• Keep the usual one-question-per-round rule, but treat mission clarity and evaluator clarity as hard readiness gates in addition to the normal ambiguity threshold.
• Once ready, do not bridge into omc-plan, autopilot, ralph, team, or the hard-deprecated omc autoresearch CLI. Instead write the mission/evaluator setup artifacts and invoke:
  • Skill("oh-my-claudecode:autoresearch")
• This handoff enters the real stateful autoresearch skill. After a successful handoff, announce the mission slug, evaluator command/script, max-runtime ceiling, and artifact location. </Autoresearch_Mode>