DE

A clarification loop to refine project goals and scope before execution.

Install

mkdir -p .claude/skills/deep-interview && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/18661" && unzip -o skill.zip -d .claude/skills/deep-interview && rm skill.zip

Installs to .claude/skills/deep-interview

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

[OMX] Socratic deep interview with mathematical ambiguity gating before execution
81 charsno explicit “when” trigger
Intermediate

Key capabilities

  • Clarify broad or ambiguous user requests
  • Identify missing concrete acceptance criteria
  • Score ambiguity quantitatively after each answer
  • Ask targeted questions about intent and boundaries
  • Generate a requirements artifact

How it works

The skill engages in a Socratic clarification loop, asking targeted questions to refine vague ideas into concrete specifications. It quantifies ambiguity to ensure alignment before execution.

Inputs & outputs

You give it
A vague idea or description of a desired change
You get back
An execution-ready specification with an explicit, testable, intent-aligned spec

When to use deep-interview

  • Clarifying ambiguous requirements
  • Interviewing for project scope
  • Refining tasks before planning

About this skill

<Purpose> Deep Interview is an intent-first Socratic clarification loop before planning or implementation. It turns vague ideas into execution-ready specifications by asking targeted questions about why the user wants a change, how far it should go, what should stay out of scope, and what OMX may decide without confirmation. </Purpose>

<Use_When>

  • The request is broad, ambiguous, or missing concrete acceptance criteria
  • The user says "deep interview", "interview me", "ask me everything", "don't assume", or "ouroboros"
  • The user wants to avoid misaligned implementation from underspecified requirements
  • You need a requirements artifact before handing off to ralplan, autopilot, ralph, or team </Use_When>

<Do_Not_Use_When>

  • The request already has concrete file/symbol targets and clear acceptance criteria
  • The user explicitly asks to skip planning/interview and execute immediately
  • The user asks for lightweight brainstorming only (use plan instead)
  • A complete PRD/plan already exists and execution should start </Do_Not_Use_When>

<Why_This_Exists> Execution quality is usually bottlenecked by intent clarity, not just missing implementation detail. A single expansion pass often misses why the user wants a change, where the scope should stop, which tradeoffs are unacceptable, and which decisions still require user approval. This workflow applies Socratic pressure + quantitative ambiguity scoring so orchestration modes begin with an explicit, testable, intent-aligned spec. </Why_This_Exists>

<Depth_Profiles>

  • Quick (--quick): fast pre-PRD pass; target threshold <= 0.30; max rounds 5
  • Standard (--standard, default): full requirement interview; target threshold <= 0.20; max rounds 12
  • Deep (--deep): high-rigor exploration; target threshold <= 0.15; max rounds 20
  • Autoresearch (--autoresearch): same interview rigor as Standard, but specialized for $autoresearch mission readiness and .omx/specs/ artifact handoff

If no flag is provided, use Standard.

<Mode_Flags>

  • --autoresearch: switch the interview into autoresearch-intake mode for $autoresearch handoff. In this mode, the interview should converge on a validator-ready research mission, write canonical artifacts under .omx/specs/, and preserve the explicit refine further vs launch boundary for downstream skill intake. </Mode_Flags> </Depth_Profiles>

<Execution_Policy>

  • Ask ONE question per round (never batch)
  • Ask about intent and boundaries before implementation detail
  • Target the weakest clarity dimension each round after applying the stage-priority rules below
  • Treat every answer as a claim to pressure-test before moving on: the next question should usually demand evidence or examples, expose a hidden assumption, force a tradeoff or boundary, or reframe root cause vs symptom
  • Do not rotate to a new clarity dimension just for coverage when the current answer is still vague; stay on the same thread until one layer deeper, one assumption clearer, or one boundary tighter
  • Before crystallizing, complete at least one explicit pressure pass that revisits an earlier answer with a deeper, assumption-focused, or tradeoff-focused follow-up
  • Gather codebase facts via explore before asking user about internals
  • When session guidance enables USE_OMX_EXPLORE_CMD, prefer omx explore for simple read-only brownfield fact gathering; keep prompts narrow and concrete, and keep ambiguous or non-shell-only investigation on the richer normal path and fall back normally if omx explore is unavailable.
  • Always run a preflight context intake before the first interview question
  • If initial context is oversized or would exceed the prompt budget, do not paste or forward the raw payload into interview prompts; request and record a prompt-safe initial-context summary first
  • The oversized initial-context summary gate is blocking: wait for the concise summary before ambiguity scoring, crystallizing artifacts, or any downstream execution handoff
  • The summary must preserve goals, constraints, success criteria, non-goals, decision boundaries, and references to any full source documents so downstream consumers receive a prompt-safe but faithful context
  • Keep total prompt payloads within a safe budget by summarizing or trimming retained history; preserve newest/highest-signal answers and never let raw oversized context crowd out the current question
  • Reduce user effort: ask only the highest-leverage unresolved question, and never ask the user for codebase facts that can be discovered directly
  • For brownfield work, prefer evidence-backed confirmation questions such as "I found X in Y. Should this change follow that pattern?"
  • In Codex CLI, deep-interview uses omx question as the required OMX-owned structured questioning path for every interview round
  • If you launch omx question in a background terminal, immediately wait for that background terminal to finish and read its JSON answer before scoring ambiguity, asking another round, or handing off
  • If omx question is unavailable in the current runtime, treat that as a blocker/error for deep-interview rather than falling back to request_user_input or plain-text questioning
  • Re-score ambiguity after each answer and show progress transparently
  • Do not hand off to execution while ambiguity remains above threshold unless user explicitly opts to proceed with warning
  • Do not crystallize or hand off while Non-goals or Decision Boundaries remain unresolved, even if the weighted ambiguity threshold is met
  • Treat early exit as a safety valve, not the default success path
  • Persist mode state for resume safety (state_write / state_read) </Execution_Policy>
<Steps>

Phase 0: Preflight Context Intake

  1. Parse {{ARGUMENTS}} and derive a short task slug.
  2. Attempt to load the latest relevant context snapshot from .omx/context/{slug}-*.md.
  3. Check whether the provided initial context or loaded snapshot is too large for safe prompt use. If it is oversized, the first interview round must ask for a concise prompt-safe summary instead of scoring ambiguity or continuing to downstream handoff.
  4. If no snapshot exists, create a minimum context snapshot with:
    • Task statement
    • Desired outcome
    • Stated solution (what the user asked for)
    • Probable intent hypothesis (why they likely want it)
    • Known facts/evidence
    • Constraints
    • Unknowns/open questions
    • Decision-boundary unknowns
    • Likely codebase touchpoints
    • Prompt-safe initial-context summary status (not_needed, needed, or recorded)
  5. Save snapshot to .omx/context/{slug}-{timestamp}.md (UTC YYYYMMDDTHHMMSSZ) and reference it in mode state.

Phase 1: Initialize

  1. Parse {{ARGUMENTS}} and depth profile (--quick|--standard|--deep).
  2. Detect project context:
    • Run explore to classify brownfield (existing codebase target) vs greenfield.
    • For brownfield, collect relevant codebase context before questioning.
  3. Initialize state via state_write(mode="deep-interview"):
{
  "active": true,
  "current_phase": "deep-interview",
  "state": {
    "interview_id": "<uuid>",
    "profile": "quick|standard|deep",
    "type": "greenfield|brownfield",
    "initial_idea": "<user input>",
    "rounds": [],
    "current_ambiguity": 1.0,
    "threshold": 0.3,
    "max_rounds": 5,
    "challenge_modes_used": [],
    "codebase_context": null,
    "current_stage": "intent-first",
    "current_focus": "intent",
    "context_snapshot_path": ".omx/context/<slug>-<timestamp>.md"
  }
}
  1. Announce kickoff with profile, threshold, and current ambiguity.

Phase 2: Socratic Interview Loop

Repeat until ambiguity <= threshold, the pressure pass is complete, the readiness gates are explicit, the user exits with warning, or max rounds are reached.

2a) Generate next question

If the initial context is oversized and no prompt-safe summary has been recorded yet, the next question must be only a summary request. Do not score ambiguity, do not run readiness gates, and do not hand off to $ralplan, $autopilot, $ralph, or $team until that summary answer is captured.

Use:

  • Original idea
  • Prior Q&A rounds
  • Current dimension scores
  • Brownfield context (if any)
  • Activated challenge mode injection (Phase 3)

Target the lowest-scoring dimension, but respect stage priority:

  • Stage 1 — Intent-first: Intent, Outcome, Scope, Non-goals, Decision Boundaries
  • Stage 2 — Feasibility: Constraints, Success Criteria
  • Stage 3 — Brownfield grounding: Context Clarity (brownfield only)

Follow-up pressure ladder after each answer:

  1. Ask for a concrete example, counterexample, or evidence signal behind the latest claim
  2. Probe the hidden assumption, dependency, or belief that makes the claim true
  3. Force a boundary or tradeoff: what would you explicitly not do, defer, or reject?
  4. If the answer still describes symptoms, reframe toward essence / root cause before moving on

Prefer staying on the same thread for multiple rounds when it has the highest leverage. Breadth without pressure is not progress.

Detailed dimensions:

  • Intent Clarity — why the user wants this
  • Outcome Clarity — what end state they want
  • Scope Clarity — how far the change should go
  • Constraint Clarity — technical or business limits that must hold
  • Success Criteria Clarity — how completion will be judged
  • Context Clarity — existing codebase understanding (brownfield only)

Non-goals and Decision Boundaries are mandatory readiness gates. Ask about them early and keep revisiting them until they are explicit.

2b) Ask the question

Use OMX-owned structured questioning via omx question for every interview round (this is the required AskUserQuestion equivalent for deep-interview) and present:

Round {n} | Target: {weakest_dimension} | Ambiguity: {score}%

{question}

omx question payload guidance for interview rounds:

  • Use canonical type values i

Content truncated.

When not to use it

  • When the request already has concrete file/symbol targets and clear acceptance criteria
  • When the user explicitly asks to skip planning/interview and execute immediately
  • When the user asks for lightweight brainstorming only

Limitations

  • Does not perform direct implementation
  • Requires user interaction for clarification
  • Relies on `omx question` for structured questioning in Codex CLI

How it compares

This skill systematically reduces ambiguity through a structured questioning process and quantitative scoring, unlike a single expansion pass that might miss critical details or user intent.

Compared to similar skills

deep-interview side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
deep-interview (this skill)03moNo flagsIntermediate
product-manager-toolkit327moReviewBeginner
task-analyzer71moNo flagsBeginner
micro-saas-launcher66moNo flagsIntermediate

Try saying

Example prompts that trigger this skill in your AI assistant.

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