deep-interview

A question-and-answer workflow that turns a vague software request into a detailed, testable specification before planning or implementation.

In plain words
What is it for?
It is for clarifying broad feature requests and preparing requirements for later planning or execution workflows.
Why use it?
It exposes missing requirements, unclear boundaries, and decisions that need the user's approval before work starts.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/orinks/accessiweather/deep-interview
Any agent
npx skills add Orinks/AccessiWeather --skill deep-interview
Clone the repo
git clone --depth 1 https://github.com/Orinks/AccessiWeather

Made for: Claude Code, Codex.

Per session 19 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 6,391 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00019 $0.06391
Opus 5 $0.00010 $0.03195
Sonnet 5 $0.00004 $0.01278
Haiku 4.5 $0.00002 $0.00639

Measured 3d ago against content hash b047982412bd, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

deep-interview scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.codex/skills/deep-interview/SKILL.md · 469 lines

How it starts

The opening of the file, as written. The whole thing — 469 lines — stays where its author put it; the contents beside it link to each section on GitHub.

<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 multiple interview rounds into one questions[] form)
  • 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 attached-tmux Codex CLI, deep-interview uses omx question as the required OMX-owned structured questioning path for every interview round
  • When invoking omx question through attached-tmux Bash/tool paths, preserve the leader-pane return target by prefixing the command with OMX_QUESTION_RETURN_PANE=$TMUX_PANE (or a concrete %pane value)
  • 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
  • Treat answers[] as the primary omx question success contract. For a single interview round, read answers[0].answer; use legacy top-level answer only as a compatibility fallback when needed.
  • If the current runtime is outside tmux and cannot render omx question, use the native structured question tool when available; otherwise ask exactly one concise plain-text question and wait for the answer
  • 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>

Read the full file on GitHub · 469 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 3d ago First seen · 469 lines · 19 tokens per session scan A b047982412bd

Subscribe to this mod's changes

deep-interview is a skill published in the GitHub repository Orinks/AccessiWeather (24 stars, last pushed 9d ago), licensed MIT. It adds 19 tokens to every session and 6,391 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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