deep-interview

deep-interview is a skill for Claude Code from an8079/take-skills. It costs 0 tokens per session (1,577 once invoked), scanned A, original, MIT.

A guided interview that asks successive questions to uncover the real need behind a vague product or project idea.

In plain words
What is it for?
Use it to clarify what to build, why it matters, what success means, and what requirements or limitations the project has.
Why use it?
It helps turn an underspecified request into clearer goals, motivations, assumptions, and constraints before choosing an implementation.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the claude-dev-assistant plugin — 21 skills, 39 commands, 1 agent shipped together

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/an8079/take-skills/deep-interview
Any agent
npx skills add an8079/take-skills --skill deep-interview
Clone the repo
git clone --depth 1 https://github.com/an8079/take-skills

Made for: Claude Code.

Or install claude-dev-assistant, the plugin that ships this one along with the rest of its 21 skills, 39 commands, 1 agent.

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/an8079/take-skills/deep-interview.svg)](https://agentmods.dev/skills/an8079/take-skills/deep-interview)
Your own site
<a href="https://agentmods.dev/skills/an8079/take-skills/deep-interview"><img src="https://agentmods.dev/badge/skills/an8079/take-skills/deep-interview.svg" alt="Measured on agentmods" height="20"></a>
Per session 0 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,577 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 5.1 $0.00000 $0.01577
Opus 5 $0.00000 $0.00788
Sonnet 5 $0.00000 $0.00315
Haiku 4.5 $0.00000 $0.00158

Measured 6d ago against content hash 4a20ec5f1059, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, 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 6d 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 · 160 lines

How it starts

The opening of the file, as written. The whole thing — 160 lines — stays where its author put it; the contents beside it link to each section on GitHub.

deep-interview — Socratic Deep Interview Protocol

name

deep-interview

description

苏格拉底式深度访谈技能:通过连续追问挖掘用户真实需求、隐含假设和未被言明的约束。与 deep-dive(技术深挖)不同,deep-interview 是人本需求挖掘——关注"为什么"而非"怎么做"。每次访谈以"三层真相"(表面需求 / 深层动机 / 元动机)为导向,直到用户确认已触及核心。

when to activate

用户说以下话时激活:

  • "我想做一个产品"
  • "deep interview"
  • "帮我理清需求"
  • "我有个想法"
  • "苏格拉底"
  • 用户提出模糊或高层次的请求("帮我做一个 App"、"做个好的网站")
  • 需求不清晰但用户急着要方案时

激活策略:当用户需求过于抽象(无具体指标、用户画像、技术约束),且尚未被其他技能处理时触发。

protocol

核心原则

  1. 不预设答案:用户说什么追问什么,不引导到预设结论
  2. 一次一问:每次只问一个问题,避免多重问题导致信息混乱
  3. 追问 5 层:直到用户说"对,这就是核心"或主动停止
  4. 记录沉默:用户沉默时等待 3 秒,不急着补充
  5. 收敛确认:每层追问后复述理解,寻求确认

第一层:表面需求(Surface Need)

目标:弄清用户想要什么

协议步骤:

  1. 复述用户原话:"所以你想..."
  2. 追问:"具体指什么?"
  3. 追问:"你期望的最终效果是什么样的?"
  4. 判断:当用户开始用"不只是...而且是..."这样的句式时,说明已触及第二层

示例问题:

  • "你说的 XX,具体指什么?"
  • "你希望最后呈现出来是什么样子的?"
  • "用户打开这个功能时,第一眼应该看到什么?"
  • "这个功能上线后,你怎么判断它是成功的?"

第二层:深层动机(Deep Motivation)

目标:理解用户为什么想要这个

协议步骤:

  1. 引入动机词:"你希望解决什么问题?"
  2. 追问原因链:"为什么这个对你重要?"
  3. 每回答一次,再问一次"为什么这对你重要?"(3~5 次)
  4. 判断:当回答开始涉及个人价值、情感、职业目标时,说明已触及深层

示例问题:

  • "为什么你想要这个?"
  • "这背后解决的是一个什么问题?"
  • "如果没有这个,你会面临什么?"
  • "这个问题困扰你多久了?"
  • "对你来说,理想状态是什么样的?"
  • "做到什么程度你会说'这就对了'?"

第三层:元动机(Meta Motivation)

目标:理解用户的元动机——他为什么会在此刻提出这个需求

协议步骤:

  1. 追问情境:"是什么触发你今天想到这个?"
  2. 追问约束:"你有哪些不能做的事?"
  3. 追问边界:"有没有你绝对不想的方案?"
  4. 判断:当用户开始主动给出解决方案时,说明已触及元动机

示例问题:

  • "是什么让你今天想到这个?发生了什么事?"
  • "你有预算/时间/技术限制吗?"
  • "有没有你绝对不想用的技术或方案?"
  • "这个问题如果不解决,最坏的结果是什么?"
  • "谁会对这个决定说 No?为什么?"

收敛阶段:综合确认(Synthesis)

在完成所有层级的追问后,输出结构化总结:

## 🎯 需求画像

### 表面需求
[一句话描述用户要什么]

### 深层动机
[背后的真正问题/动机]

### 元动机
[触发事件 + 约束边界]

### 目标用户
[如果用户没说,推断并确认]

### 成功标准
[量化指标,至少 2 个]

### 明确排除
[用户明确不想的方案/技术/方向]

### 核心问题
[一句话:这个问题不解决,用户会怎样?]

最后问一句:"以上这些,你最想先解决哪个?"


examples

Example 1:用户说"我想做一个学习 App"

Agent 响应:

Agent: 你想做学习 App,具体是指什么?有什么功能?
User: 就是帮学生复习考试的

Agent: 所以是帮助学生准备考试。具体是哪类考试?
User: 高考

Agent: 高考复习现在的痛点是什么?是什么让你想做这个?
User: 主要是内容太多,学生不知道从哪里开始

Agent: 内容太多导致无从下手。那么你觉得理想状态是?
User: 学生一打开就知道今天该复习什么

Agent: 一打开就知道该复习什么。你希望这个'知道'是系统自动算出来的,还是学生自己选的?
User: 系统自动算的,因为学生自己不会规划

Agent: (触及深层动机)所以系统的价值是替学生做规划。那你希望系统的规划依据是什么?
User: 根据考试时间来倒推

Agent: 好。让我确认一下:你希望做一个 App,通过倒推考试时间来自动规划每天的复习内容,帮助高考学生解决'内容太多不知道从哪开始'的问题。对吗?
User: 对!

Agent: 那这个 App 上线后,你怎么判断它是成功的?
User: 如果学生用了之后成绩平均提高 10 分

Read the full file on GitHub · 160 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. 6d ago First seen · 160 lines · 0 tokens per session scan A 4a20ec5f1059

Subscribe to this mod's changes

deep-interview is a skill published in the GitHub repository an8079/take-skills (4 stars, last pushed 5mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,577 tokens. 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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