Gr User Interview

Community

Gingiris-1031

gr-user-interview

用户访谈与需求验收框架，覆盖原始证据、方案、优先级及回访交接。HeyGen 做 937 次访谈找到 PMF 的完整方法论。覆盖访谈招募、问题设计、访谈执行、数据合成、决策驱动。从 gingiris-user-interview 提炼。

Overview

Publisher	Gingiris-1031
Repository	`gingiris-skills`
Skill name	`gr-user-interview`
Stars	88
Forks	6
Bundled files	4
License	MIT
Links	Skill source Repository

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.
4 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by Gingiris-1031 on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/Gingiris-1031/gingiris-skills/tree/main/skills/gr-user-interview
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.

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/Gingiris-1031/gingiris-skills.git /tmp/gingiris-skills
mkdir -p .claude/skills
cp -r /tmp/gingiris-skills/skills/gr-user-interview .claude/skills/gr-user-interview

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Gr User 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 Gr User 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 Gr User 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.

⚠️ 2C 产品的渠道调整

本 skill 默认 dev / B2B 渠道。2C 消费品 / 教育 / 应用：获客主战场是 垂类社区 + 短视频 + 垂直 KOL，按地区公开数据选第一平台（如印尼/泰国短视频已反超 Facebook）。KOL 优先 nano / micro 垂类——粉丝越多互动率越低，micro > mega 性价比更高。完整 2C 渠道数据库 + 公开来源见 → gingiris-seo-geo/references/2c-adaptation.md。

gr-user-interview — 用户访谈

什么时候用

"我要找 PMF，怎么做访谈"
"用户反馈很杂，不知道抓哪个"
"我采访了 5 个人，都没 insight"
"怎么避免用户说假话"
"不知道该访谈谁 / 约不到人"
"激活率、付费率该怎么定义和对标"

从访谈到上线验收

访谈之后需要与产研协作时，读取需求交接与回访模板：分开原始证据和拟议方案，按基础能力、onboarding、商业化归类，给出优先级理由、负责人、可观察的验收结果和回访对象。bug 与 feature request 使用不同视图，不能把收集到的意见直接当成交付承诺。

核心原则（5 条）

问 behavior，不问 opinion ❌ "你会用这个功能吗？" → 会得到礼貌性"yes" ✅ "上次遇到 X 问题是什么时候？你当时做了什么？" → 得到真实行为
20 分钟访谈 >> 5 个 feature 问题 深度一个场景，比问 5 个浅问题更有价值
录音 / 整稿 > 当场记笔记 当场记会错过微表情和语气
5 人后暂停，复盘 不要连访 20 人才总结，5 人后大概率已看到模式
用词镜像 用户用什么词，你也用什么词 —— 不要替换成产品语言

访谈结构模板（20-30 分钟）

阶段	时长	问什么
Warm-up	2 min	"你今天做了什么？"（开放式，松弛下来）
Discover	5 min	"上次做 X 是什么时候？能讲讲吗？"
Deep-dive	10 min	"当时你用了什么工具？怎么用的？哪里卡住？"
Alternative	5 min	"有没有试过其他方法？为什么没继续？"
Wrap-up	3 min	"如果有一个魔法能解决这个问题，它会怎么工作？"

Token Top10 访谈法（招牌打法）

后台用量 Top10 拉 list → 共享屏幕访谈（必录屏）→ transcript 喂 AI 提 feature/bug 进 Linear → 次日回访。每天 ≥2 场。

按 token 消耗 / 用量排 Top10，每周刷新——重度用户的反馈密度最高
必须共享屏幕 + 录屏：看真实操作 > 听口述
Transcript 交给 AI 自动提取 feature request / bug，直接进 issue 工具
次日回访"你提的 X 已修/已排期"——反馈闭环就是留存杠杆
访谈后截 5-10 分钟关键片段给产研

邀约节奏：

1/3/7/14 天四次触达（Day 1 → 3 → 7 → 14 各跟进一次）
双通道：邮件模板 + 邮箱反查 LinkedIn 私信，邮件没回转 LinkedIn 补一轮
话术用宽泛理由（"早期团队想做访谈"），不暴露真实筛选原因——避免用户表演

组合技：

KOL 访谈二合一：合作 YouTuber 顺便做用户访谈 + 可用性测试，一次合作两份价值（20-30 分钟视频访谈 ROI 最高）
Wisprflow 打法：launch 前人工 onboard 前 500 用户逐个访谈，用势能带 launch

激活基准速查

项	基准
激活定义	必须是行为阈值：完成 ≥2 任务 / 连续 3 天 ≥20 轮对话 / 使用超 5 分钟——不是"登录过"
注册 → 付费合格线	>5%（Pro-C 5-8%、SMB 10-15%）
流失定义	两次对话后 24h 不回访 = 流失 → 自动触发挽回邮件
付费归因	四时间戳交叉：注册 × 激活 × 支付 × 最近登录

深度参考

📂 https://github.com/Gingiris-1031/gingiris-skills/tree/main/skills/gingiris-user-interview

references/templates.md — 不同阶段（Discovery / Validation / Retention）的问题模板
HeyGen 937 访谈案例拆解

合成流程

每次访谈后 24h 内整理 3 张卡片：
- Pain（用户的真实痛）
- Workaround（他们目前怎么凑合）
- Quote（可引用的原话）
每 5 个访谈做一次复盘：
- 重复出现 ≥ 3 次的 Pain → 候选 PMF 方向
- 重复出现 ≥ 2 次的 Workaround → 竞品信号
每 20 个访谈做一次决策：
- 继续这个假设 / 换方向 / 需要更多数据

级联推荐

访谈发现新 PMF 方向 → gr-blog-post 写一篇（验证市场反应）
访谈发现对手名字被提 ≥ 3 次 → gr-competitor 深度分析
PMF 明确 → gr-b2b-growth 或 gr-oss-marketing 启动增长
需要招募访谈对象 → 从 gr-seo-patrol 的 GSC 流量挑读者，或直接上「Token Top10 访谈法」拉后台用量榜
在跑红人合作 → 用「KOL 访谈二合一」顺路拿访谈（红人打法见 gingiris-kol-outreach）
访谈后要定激活/付费目标 → 对照上方「激活基准速查」先把行为阈值写死

反模式

❌ 问 "你愿意付费吗" → 100% 是假答案
❌ 发问卷替代访谈（信息密度差 10x）
❌ 只访现有用户不访流失用户（流失才是真金）
❌ 一次访谈超过 45 分钟（疲劳 → 敷衍）
❌ 访谈中推销自己产品（污染答案）
❌ 邀约只发一次就放弃（1/3/7/14 四次触达才是基线）
❌ 邀约话术暴露筛选原因（"因为你用量高所以找你" → 用户开始表演）
❌ 激活定义写成"注册/登录过"（必须是行为阈值）
❌ 访谈完不回访（次日回访"已修/已排期"是最便宜的留存动作）

加入出海社群与购买资料包 · Community & Resources

加入出海社群： 微信添加 staywildgrow，留言「加入社群」即可。
购买资料包： 微信添加 staywildgrow，咨询购买 Gingiris 出海资料包，价格 599 元人民币。
Join the global growth community: Add staywildgrow on WeChat and send “加入社群” (join the community).
Get the resource pack: Add staywildgrow on WeChat to purchase the Gingiris global growth resource pack for CNY 599.

社群与资料包为可选入口，不影响本开源 Skill 的安装和使用。

The community and paid resource pack are optional; neither is required to install or use this open-source skill.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Gr User Interview AI skill do?

Why use Gr User Interview on TypingMind?

Because you install it once and use it with any model. Gr User 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 Gr User Interview in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Gingiris-1031/gingiris-skills/tree/main/skills/gr-user-interview. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Gr User 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 Gr User Interview?

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

Is the Gr User Interview AI skill free?

Yes. It is published on GitHub by Gingiris-1031 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

⚠️ 2C 产品的渠道调整

gr-user-interview — 用户访谈

什么时候用

从访谈到上线验收

核心原则（5 条）

访谈结构模板（20-30 分钟）

Token Top10 访谈法（招牌打法）

激活基准速查

深度参考

合成流程

级联推荐

反模式

加入出海社群与购买资料包 · Community & Resources

Bundled files

Frequently asked questions

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