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.
npx skills add Gingiris/gingiris-skills --skill gr-user-interviewgit clone --depth 1 https://github.com/Gingiris/gingiris-skillsWrote 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.
[](https://agentmods.dev/skills/gingiris/gingiris-skills/gr-user-interview)<a href="https://agentmods.dev/skills/gingiris/gingiris-skills/gr-user-interview"><img src="https://agentmods.dev/badge/skills/gingiris/gingiris-skills/gr-user-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.
<a href="https://agentmods.dev/skills/gingiris/gingiris-skills/gr-user-interview"><img src="https://agentmods.dev/badge/skills/gingiris/gingiris-skills/gr-user-interview.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00058 | $0.00919 |
| Opus 5.5 | $0.00023 | $0.00368 |
| Sonnet 5.5 | $0.00012 | $0.00184 |
| Haiku 4.5 | $0.00006 | $0.00092 |
Grade A, and why
gr-user-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.
What it actually says
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"
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？" |
深度参考
📂 https://github.com/Gingiris/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
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）
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.
- 1mo ago First seen · 94 lines · 58 tokens per session scan A 5238d5dea9bd
gr-user-interview is a skill published in the GitHub repository Gingiris/gingiris-skills (23 stars, last pushed 5mo ago), licensed MIT. It adds 58 tokens to every session and 919 once invoked, about $0.0002 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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