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Pm Interview

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konglong87
pm-interview

Use when: 需要设计用户访谈/用户调研方案、编写访谈提纲、规划样本与招募、制定访谈执行与分析方法 Do NOT use when: 仅做案头需求调研(用 pm-demand);已有明确结论只需验证单一假设且无需访谈

Overview

Publisherkonglong87
RepositorysuperPM
Skill namepm-interview
Stars
86
Forks
8
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by konglong87 on GitHub. Read the source before you install it.

Installation

Install the Pm Interview AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/konglong87/superPM/tree/main/skills/01-demand-insight/pm-interview
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/konglong87/superPM.git /tmp/superPM
mkdir -p .claude/skills
cp -r /tmp/superPM/skills/01-demand-insight/pm-interview .claude/skills/pm-interview
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pm Interview in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Pm Interview on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Pm Interview is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Preamble (run first)

bash
if [ -f "docs/01-需求调研/需求调研报告.md" ]; then
  echo "✅ 需求调研报告 - 已找到(可复用假设与目标用户)"
else
  echo "⏳ 需求调研报告 - 未找到(可选,缺失时由本技能快速采集)"
fi

写入时边界

只讨论时不写文件,启动检查不创建目录。用户要求保存产物时,先确认当前产品、精确输出路径及已有文件的覆盖/追加选择;确认后仅在实际 Write 前创建该输出文件的父目录。完整安装可参见 skills/_shared/artifact-policy.md,单独安装本 skill 也按本段执行。


跨 Agent 交互规则

当流程要求与用户交互时:

  1. 如果当前环境支持 AskUserQuestion,使用 AskUserQuestion(最佳体验)。
  2. 如果当前环境不支持 AskUserQuestion,必须用普通聊天消息提出同样问题。
  3. 一次只问一个问题。
  4. 提问后必须停止当前回合,等待用户回答(STOP and WAIT)。
  5. 不得在用户回答前生成文档、写入 docs。
  6. 已有 docs 文件不能替代本轮用户回答。

适用场景

  • 用户说"用户访谈""访谈提纲""用户调研方案""想找用户聊聊""定性研究"
  • 与 pm-demand 区分:pm-demand 偏案头/二手调研;本技能偏一手定性研究(访谈设计与执行)。

执行流程

步骤 1: 明确访谈目标与假设(主 agent - 用户交互)

使用 AskUserQuestion 询问:

🎯 访谈目标

这次访谈主要想搞清楚什么?

A) 验证需求假设(功能是否真被需要) B) 探索用户痛点与场景(开放式发现) C) 评估产品体验(可用性/满意度) D) 理解决策与付费(选型/付费动机)

你想验证/探索的核心假设是?(一句话)

记录到变量 INTERVIEW_GOAL 与 HYPOTHESIS。


步骤 2: 确定访谈对象与样本(主 agent)

结合需求调研报告(如有)提取目标用户画像,使用 AskUserQuestion 确认:

👥 访谈对象

A) 沿用需求调研报告的目标用户分层 B) 我手动指定人群 C) 聚焦某一极端/核心用户群

样本量:建议 5-8 人/每细分(饱和即止);周期:{周}

记录 SEGMENTS 与 SAMPLE_PLAN。


步骤 3: 设计访谈提纲(主 agent + 可选 subagent)

按阶段生成提纲,默认结构:

  1. 暖场(建立信任,背景了解)
  2. 行为与场景(真实使用/替代方案)
  3. 痛点与动机(当前如何解决问题、痛点强度)
  4. 需求与期望(对解决方案的期望、付费意愿)
  5. 收尾(开放补充、是否愿意后续回访)

使用 Agent 工具(可选,针对复杂主题)派发 subagent 生成分群提纲:

Tool: Task
Parameters:
  subagent_type: "general-purpose"
  description: "访谈提纲生成"
  prompt: |
    你是用户研究专家。请基于以下信息设计一份用户访谈提纲。
    目标:{INTERVIEW_GOAL}
    核心假设:{HYPOTHESIS}
    对象分层:{SEGMENTS}
    要求:开放式问题为主、避免引导性提问、每阶段 3-5 题、标注追问点。
    输出 Markdown 提纲。

主 agent 整合并定稿。


步骤 4: 招募与执行指引(主 agent)

生成:

  • 招募话术 / 筛选问卷(含准入/排除标准)
  • 执行指引:提问技巧(追问"为什么""能举个例子吗")、避免 Leading、录音与知情同意、时间控制(45-60 分钟)
  • 记录模板:逐场要点 + 引用原话

步骤 5: 分析与产出(主 agent)

使用 Write 工具生成 docs/01-需求调研/用户访谈方案.md:

markdown
# {产品名称} 用户访谈方案

## 一、访谈目标与假设
- 目标: {INTERVIEW_GOAL}
- 核心假设: {HYPOTHESIS}
- 待回答的关键问题: {列表}

## 二、对象与样本
| 分层 | 特征 | 样本量 | 招募渠道 |
|------|------|-------|---------|
| {层1} | {特征} | {n} | {渠道} |

## 三、访谈提纲(按阶段)
### 阶段1 暖场
1. {问题}
### 阶段2 行为与场景
1. {问题}(追问:...)
...

## 四、招募与执行
- 招募话术: {话术}
- 执行指引: {要点}
- 记录模板: {模板}

## 五、分析方法
- 归纳编码(开放→主题)
- 假设验证矩阵(支持/反驳/需补充)
- 输出物: 洞察报告 + 用户原话引用

## 六、下一步建议
1. /pm-demand - 整合访谈洞察进入需求调研
2. /pm-clarify - 细化高价值需求
3. /pm-priority - 将洞察转优先级

步骤 6: 推荐下一步(主 agent)

✅ 用户访谈方案已生成:docs/01-需求调研/用户访谈方案.md

建议执行:

  1. /pm-demand - 整合访谈洞察
  2. /pm-clarify - 细化需求
  3. /pm-priority - 转优先级

输出质量对比

✅ Good 示例:

  • 开放式问题:「上次你是怎么处理 XX 的?能举个具体例子吗?」
  • 有样本计划与准入标准
  • 有分析方法(编码/假设矩阵)

❌ Bad 示例:

  • 引导性提问:「你是不是觉得 XX 不好用?」
  • 无样本规划
  • 无分析方法

常见误区 / Red Flags — STOP

误区正确做法
用引导性问题开放式、中立、追问"为什么"
样本无分层按用户分层招募,避免同质化
只收集不分析必须定义编码与假设验证方法
拿个例当结论多样本交叉验证,引用原话

产出质量检查 / Verification Checklist

  • 已明确访谈目标与假设
  • 已确定对象分层与样本计划
  • 已生成分阶段开放式提纲
  • 已给出招募与执行指引
  • 输出文档已生成到 docs/01-需求调研/
  • 已推荐 2-3 个后续 skill

⚠️ 任何一项未通过 → 补全后再标记完成。


Frequently asked questions

What does the Pm Interview AI skill do?

Use when: 需要设计用户访谈/用户调研方案、编写访谈提纲、规划样本与招募、制定访谈执行与分析方法 Do NOT use when: 仅做案头需求调研(用 pm-demand);已有明确结论只需验证单一假设且无需访谈

Why use Pm Interview on TypingMind?

Because you install it once and use it with any model. Pm Interview is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Pm Interview in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/konglong87/superPM/tree/main/skills/01-demand-insight/pm-interview. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Pm Interview?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Pm Interview?

As many as you like. As long as a model supports skills, you can use Pm Interview with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Pm Interview AI skill free?

Yes. It is published on GitHub by konglong87 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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