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 agentmods add commands/killvxk/pm-skills-zh/research-usersgit clone --depth 1 https://github.com/killvxk/pm-skills-zhWrote 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/commands/killvxk/pm-skills-zh/research-users)<a href="https://agentmods.dev/commands/killvxk/pm-skills-zh/research-users"><img src="https://agentmods.dev/badge/commands/killvxk/pm-skills-zh/research-users.svg" alt="Measured on agentmods" 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 | $0.00026 | $0.01180 |
| Opus 5 | $0.00013 | $0.00590 |
| Sonnet 5 | $0.00005 | $0.00236 |
| Haiku 4.5 | $0.00003 | $0.00118 |
Grade A, and why
research-users 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.
What it actually says
/research-users -- 用户调研综合分析
将原始调研数据转化为可落地的用户画像、行为细分与客户旅程地图。支持输入问卷数据、访谈记录、反馈、数据分析报告，或直接输入产品描述进行探索性调研。
调用方式
/research-users [上传问卷结果、访谈记录或反馈数据]
/research-users 面向代理商的 B2B 项目管理工具——帮我了解我们的用户
/research-users [粘贴用户反馈或支持工单数据]
工作流程
第一步：接收调研输入
支持以下任意组合：
- 问卷回复（CSV、电子表格、粘贴文本）
- 访谈记录或访谈稿
- 支持工单或功能需求
- 产品数据分析 / 行为数据
- NPS 或满意度数据
- 产品描述（无数据时用于探索性调研）
询问用户：
- 你有哪些调研资料？格式是什么？
- 你想了解什么？（用户是谁、他们有何不同、摩擦点在哪里）
- 这些发现将用于哪些决策？（路线图、定位、定价、新用户引导）
第二步：构建用户画像
应用 user-personas 技能：
- 从数据中识别 3-4 个差异明显的用户画像
- 每个画像包含：姓名、角色、目标（JTBD）、痛点、收益、行为规律
- 包含出乎意料的洞察——数据中令你惊讶的发现
- 注明每个画像的占比（如数据允许，标注其在用户群中的比例）
第三步：细分用户
应用 user-segmentation 和 market-segments 技能：
- 创建行为细分（不只是人口统计细分）
- 每个细分市场包含：规模、JTBD、产品契合度、付费意愿、参与度
- 识别价值最高的细分市场和增长最快的细分市场
- 将细分市场与画像进行映射（梳理重叠关系）
第四步：绘制客户旅程地图
应用 customer-journey-map 技能：
- 绘制端到端旅程：认知 → 考量 → 新用户引导 → 活跃使用 → 扩展 → 口碑传播
- 每个阶段包含：触点、情绪、痛点、顿悟时刻
- 识别最大的流失节点
- 突出值得放大的愉悦时刻
第五步：生成调研报告
## 用户调研报告：[产品]
**日期**：[今天]
**数据来源**：[分析了哪些内容]
**样本量**：[如适用]
### 执行摘要
[3-5 句话：核心发现与启示]
### 用户画像
#### 画像 1：[姓名] — "[能代表他们的一句话]"
- **是谁**：[角色、背景、经验水平]
- **首要 JTBD**：[当……时，我想……，这样我就能……]
- **主要痛点**：[前 3 项]
- **主要收益**：[让他们满意的事]
- **行为规律**：[他们如何使用产品]
- **占比**：[X% 的用户群]
[对每个画像重复上述结构]
### 用户细分
| 细分市场 | 规模 | 首要 JTBD | 产品契合度 | 价值 | 增长潜力 |
|----------|------|-----------|-----------|------|---------|
### 客户旅程地图
| 阶段 | 触点 | 情绪 | 痛点 | 改进机会 |
|------|------|------|------|---------|
### 核心洞察
1. [洞察及支撑证据]
2. ...
### 建议
1. [与发现相结合的可行建议]
2. ...
### 待解答的问题
[数据未能回答的问题——建议跟进的调研方向]
保存为 markdown 格式。
第六步：提供后续建议
- "要我设计访谈提纲，针对某个具体画像深入挖掘吗？"
- "要我对这些细分市场做情感分析吗？"
- "要我为头部画像构建价值主张吗？"
- "要我将旅程地图中的痛点整理为功能机会，进行优先级排序吗？"
注意事项
- 如果数据不足，请如实说明置信度——5 次访谈只能产生假设，而非结论
- 画像应该实用，而非装饰——每个画像都应能影响某项产品决策
- 行为细分比人口统计细分对产品决策更有价值
- 旅程地图应呈现情绪，而非只有动作——用户在哪里感到沮丧、在哪里感到愉悦，才是优先级排序的依据
- 如果没有提供任何数据，生成基于调研经验的假设，并建议验证方法
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.
- 6d ago First seen · 122 lines · 26 tokens per session scan A fcdfbdb3676f
research-users is a command published in the GitHub repository killvxk/pm-skills-zh (151 stars, last pushed 5mo ago), licensed MIT. It adds 26 tokens to every session and 1,180 once invoked, about $0.0001 per session on Opus 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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.