deep-interview

<Purpose_And_Principles> DIPP-1 — Purpose. Deep Interview applies Socratic questioning with mathematical ambiguity scoring to replace vague ideas with crystal-clear specifications: it exposes hidden assumptions, measures clarity across weighted dimensions, and refuses to proceed until ambiguity drops below the resolved threshold for this run. The output feeds into a gated pipeline: deep-interview → ralplan consensus refinement → pending approval → explicitly approved execution, ensuring maximum clarity before any mutation starts. AI can build anything. The hard part is knowing what to build. GJC planning 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 iteratively exposes assumptions and mathematically gates 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.

DIPP-2 — Use when.

Use when the user wants requirements clarified before execution: a vague or exploratory idea ("I have a vague idea", "not sure exactly what I want"); an explicit request to interview ("deep interview", "interview me", "ask me everything", "don't assume", "make sure you understand", "socratic"); a wish to avoid "that's not what I meant" outcomes from autonomous execution or to reach mathematically-validated clarity before committing to execution; a task complex enough that jumping to code would waste cycles on scope discovery; an implementation ask whose target, scope, acceptance criteria, or safety boundary is ambiguous enough that mutation would require guessing; or an explicit deep-interview request even after being told the request is already clear.

• User requests a trace/research pre-step before the interview, e.g. /skill:deep-interview --trace <idea>

DIPP-3 — Question pacing.

• Ask ONE question at a time -- never batch multiple questions

DIPP-4 — Language.

• Default to English when no language preference is explicit or obvious. Preserve the user/session language for every user-facing announcement, topology confirmation, option label, and interview question when state includes language.instruction; do not add language-specific special cases

DIPP-5 — Self-proofread.

• Before emitting any user-facing natural-language prose governed by language.instruction, perform one silent, best-effort self-proofread in the preserved session language for obvious spelling, spacing, grammar, inflection/particle, and word-choice errors, using the same language-agnostic pass for whatever language is active rather than special-casing any single language. Apply it only to newly generated prose and never announce the proofreading, show before/after text, apologize for it, or re-emit a corrected copy. Do not alter code blocks or identifiers, file paths, CLI commands, JSON/configuration keys, ask metadata keys, table/round structure, fixed labels, numeric scores, component ids, status tokens, user quotes or source text, Phase 0 threshold markers such as Deep Interview threshold: <resolvedThresholdPercent> (source: <resolvedThresholdSource>), or fixed paths such as .gjc/_session-{sessionid}/specs/deep-interview-{slug}.md; still apply the self-proofread to generated natural-language clauses or cells inside those structures, including Why now rationale, gap text, next-target phrasing, and coverage notes

DIPP-6 — Weakest dimension.

• 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

DIPP-7 — Prompt budget.

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 or likely to crowd out downstream prompts, create a concise prompt-safe summary first — one that preserves user intent, decisions, constraints, unknowns, cited files/symbols, and any explicit non-goals — and wait until that summary exists before ambiguity scoring, weakest-dimension selection, question generation, brownfield exploration prompts, spec crystallization, or any downstream execution handoff (bridge to ralplan or ultragoal).