deep-interview

deep-interview is a skill for Claude Code, Codex from mrzhangguoguo/oh-my-workbuddy. It costs 24 tokens per session (4,033 once invoked), scanned A, original, MIT.

A guided question-and-answer process that turns a vague coding request into a detailed specification before planning or implementation.

In plain words
What is it for?
Use it to clarify broad requests, gather acceptance criteria, and produce a requirements document for later planning or coding.
Why use it?
It reduces the risk of building the wrong thing when the goal, boundaries, or success conditions are unclear.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions CLAUDE.md; names the AskUserQuestion tool; mentions AGENTS.md.

Good fit Use it to clarify broad requests, gather acceptance criteria, and produce a requirements document for later planning or coding.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/mrzhangguoguo/oh-my-workbuddy/deep-interview
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.

Any agent
npx skills add mrzhangguoguo/oh-my-workbuddy --skill deep-interview
Clone the repo
git clone --depth 1 https://github.com/mrzhangguoguo/oh-my-workbuddy

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for deep-interview

README.md
[![agentmods](https://agentmods.dev/badge/skills/mrzhangguoguo/oh-my-workbuddy/deep-interview/github.svg)](https://agentmods.dev/skills/mrzhangguoguo/oh-my-workbuddy/deep-interview)
Your own site
<a href="https://agentmods.dev/skills/mrzhangguoguo/oh-my-workbuddy/deep-interview"><img src="https://agentmods.dev/badge/skills/mrzhangguoguo/oh-my-workbuddy/deep-interview/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for deep-interview

Your own site · 80×15
<a href="https://agentmods.dev/skills/mrzhangguoguo/oh-my-workbuddy/deep-interview"><img src="https://agentmods.dev/badge/skills/mrzhangguoguo/oh-my-workbuddy/deep-interview.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 24 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,033 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00024 $0.04033
Opus 5 $0.00012 $0.02017
Sonnet 5 $0.00005 $0.00807
Haiku 4.5 $0.00002 $0.00403

Measured 10d ago against content hash fbd733e9238e, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, 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 10d 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.

skills/deep-interview/SKILL.md · 254 lines

How it starts

The opening of the file, as written. The whole thing — 254 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Ported from oh-my-codex deep-interview. OMX runtime conventions ($macro invocation, omx CLI, .omx/ state directory) are replaced with WorkBuddy idioms (Skill tool, Agent tool, task list, .workbuddy/memory).

<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 plan, team, or an execution workflow. </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 spec/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 downstream work begins with an explicit, testable, intent-aligned spec. </Why_This_Exists>

<Depth_Profiles>

  • Quick (--quick): fast pre-spec 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.

Profile max rounds is a hard cap, not a target. Do not continue only to reach a numbered round count.

If no flag is provided, use Standard. </Depth_Profiles>

<Execution_Policy>

  • Ask ONE question per round (never batch multiple interview rounds into one AskUserQuestion call).
  • Ask about intent and boundaries before implementation detail.
  • Target the weakest clarity dimension each round (see 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 repo inspection tools (Read, Grep, Glob, Bash) before asking the user about internals.
  • Always run a preflight context intake before the first interview question.
  • For brownfield work, preflight must include doc/context grounding before user-facing questions: inspect applicable AGENTS.md / CLAUDE.md files, README/getting-started docs, relevant docs/ contracts/plans/ADRs, and any project-local glossary/context files such as CONTEXT.md or CONTEXT-MAP.md when present.
  • Treat existing repo language as evidence, not authority: if the user uses a fuzzy, overloaded, or conflicting term, surface the specific doc/code wording and ask which meaning should govern.
  • Cross-check user claims about current behavior against code or documented contracts when discoverable. If docs and code disagree, ask a confirmation question that names both sources.
  • Use scenario-based edge-case grilling when relationships, boundaries, or handoff behavior are unclear: invent one concrete scenario that stresses the ambiguous boundary, then ask one focused question about the expected outcome.
  • Durable docs, glossary, ADR, or memory updates are opt-in and public-safe only. Recommend such updates in the handoff summary, but do not automatically create public docs from interview transcripts unless the user explicitly chooses that as in-scope.
  • Reduce user effort: ask only the highest-leverage unresolved question, and never ask the user for codebase facts that can be discovered directly.
  • When unresolved ambiguity depends on current external best practices, invoke the best-practice-research skill as the bounded evidence wrapper before crystallizing requirements or handing off.
  • Use these transcript/spec labels:
    • [from-code][auto-confirmed] — exact, high-confidence codebase facts from manifests/configs or direct source evidence, with no prescription attached.
    • [from-code] — codebase findings that are useful but inferred, pattern-based, or low/medium confidence and therefore need a confirmation-style user-facing round.
    • [from-research] — externally sourced facts (API limits, compatibility, public docs); facts only, not decisions.
    • [from-user] — goals, preferences, business logic, scope, non-goals, acceptance criteria, tradeoffs, and any decision-bearing interpretation.
  • Treat [from-code][auto-confirmed] and other non-user fact discoveries as context/transcript updates, not interview rounds: do not increment the user-facing round number for facts the agent can safely establish.
  • Auto-confirm only descriptive facts. If a finding implies what the feature should do, which pattern to follow, which tradeoff to accept, or what should stay in/out of scope, route the entire decision-bearing question to the user as [from-user].
  • Use the AskUserQuestion tool for every structured interview round. If it is unavailable, ask exactly one concise plain-text question in chat and wait for the answer.
  • Re-score ambiguity after each answer and show progress transparently.
  • Once ambiguity is at or below the active profile threshold, stop ordinary questioning. Run the practical closure audit: crystallize/handoff when readiness gates pass; otherwise ask only the final closure question needed to satisfy a named gate.
  • Treat max_rounds as a stop cap, not evidence that more rounds are needed.
  • Do not hand off to execution while ambiguity remains above threshold unless the 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.
  • Persist progress with the task list (TaskCreate/TaskUpdate) and/or append to .workbuddy/memory/YYYY-MM-DD.md; do not rely on any persistent "mode" file. </Execution_Policy>

Read the full file on GitHub · 254 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. 10d ago First seen · 254 lines · 24 tokens per session scan A fbd733e9238e

Subscribe to this mod's changes

deep-interview is a skill published in the GitHub repository mrzhangguoguo/oh-my-workbuddy (2 stars, last pushed 2mo ago), licensed MIT. It adds 24 tokens to every session and 4,033 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 2026-08-31.

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