pm-interview

pm-interview is a skill for Claude Code from konglong87/superPM. It costs 75 tokens per session (1,928 once invoked), scanned A, original, MIT.

A user-interview and research planning workflow, with instructions in Chinese, for defining goals, participants, interview questions, and analysis methods.

In plain words
What is it for?
Use it to plan interviews about needs, pain points, product experience, or buying decisions, including participant segments and sample sizes.
Why use it?
It provides structure for learning directly from users instead of relying only on assumptions or desk research.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: mentions subagents; names the AskUserQuestion tool.

Part of the super-pm plugin — 58 skills, 1 hook shipped together

Good fit Use it to plan interviews about needs, pain points, product experience, or buying decisions, including participant segments and sample sizes.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/konglong87/superpm/pm-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 konglong87/superPM --skill pm-interview
Clone the repo
git clone --depth 1 https://github.com/konglong87/superPM

Made for: Claude Code.

Or install super-pm, the plugin that ships this one along with the rest of its 58 skills, 1 hook.

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 pm-interview

README.md
[![agentmods](https://agentmods.dev/badge/skills/konglong87/superpm/pm-interview/github.svg)](https://agentmods.dev/skills/konglong87/superpm/pm-interview)
Your own site
<a href="https://agentmods.dev/skills/konglong87/superpm/pm-interview"><img src="https://agentmods.dev/badge/skills/konglong87/superpm/pm-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 pm-interview

Your own site · 80×15
<a href="https://agentmods.dev/skills/konglong87/superpm/pm-interview"><img src="https://agentmods.dev/badge/skills/konglong87/superpm/pm-interview.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 75 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,928 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00075 $0.01928
Opus 5.5 $0.00030 $0.00771
Sonnet 5.5 $0.00015 $0.00386
Haiku 4.5 $0.00007 $0.00193

Measured 1mo ago against content hash 687c4d1c06c8, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

pm-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 1mo 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/01-demand-insight/pm-interview/SKILL.md · 221 lines

How it starts

The opening of the file, as written. The whole thing — 221 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Preamble (run first)

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:

# {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

Read the full file on GitHub · 221 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. 1mo ago Changed · +2 lines 687c4d1c06c8
  2. 1mo ago First seen · 219 lines · 75 tokens per session scan A 22f39bcedb61

Subscribe to this mod's changes

pm-interview is a skill published in the GitHub repository konglong87/superPM (83 stars, last pushed yesterday), licensed MIT. It adds 75 tokens to every session and 1,928 once invoked, about $0.0003 per session on Opus 5.5. 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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