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 echoVic/boss-skill --skill user-researchgit clone --depth 1 https://github.com/echoVic/boss-skillWrote 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/echovic/boss-skill/user-research)<a href="https://agentmods.dev/skills/echovic/boss-skill/user-research"><img src="https://agentmods.dev/badge/skills/echovic/boss-skill/user-research/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/echovic/boss-skill/user-research"><img src="https://agentmods.dev/badge/skills/echovic/boss-skill/user-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00028 | $0.02144 |
| Opus 5.5 | $0.00011 | $0.00858 |
| Sonnet 5.5 | $0.00006 | $0.00429 |
| Haiku 4.5 | $0.00003 | $0.00214 |
Grade A, and why
pm/user-research 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 24d 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.
How it starts
The opening of the file, as written. The whole thing — 247 lines — stays where its author put it; the contents beside it link to each section on GitHub.
用户研究方法论
适用场景
在产品设计前，需要深入理解目标用户是谁、他们的特征是什么、他们在什么场景下使用产品、他们的行为路径是什么。
核心方法
1. 用户画像 (User Persona)
用户画像是对目标用户的具象化描述，帮助团队建立对用户的共同理解。
用户画像模板
用户画像 1：[给用户起个名字，如"技术小白张三"]
-
基本特征：
- 年龄：[年龄段]
- 职业：[职业]
- 收入：[收入水平]
- 教育：[教育背景]
- 地域：[所在地区]
-
行为特征：
- 使用习惯：[如何使用类似产品]
- 技术水平：[对技术的熟悉程度]
- 使用频率：[多久使用一次]
- 使用时长：[每次使用多久]
- 设备偏好：[PC/移动端/平板]
-
心理特征：
- 性格特点：[如保守/激进、理性/感性]
- 价值观：[看重什么]
- 动机：[为什么使用产品]
- 顾虑：[担心什么]
-
核心需求：
- 最想解决的问题：[核心痛点]
- 期望的结果：[想要达成什么]
- 愿意付出的代价：[时间/金钱/学习成本]
-
痛点场景：
- 场景描述：[具体的使用场景]
- 当前解决方案：[现在怎么解决]
- 痛点：[现有方案的问题]
- 情绪：[遇到痛点时的感受]
-
期望体验：
- 理想流程：[希望如何完成任务]
- 关键体验点：[最在意的体验]
- 成功标准：[什么样算成功]
创建用户画像的步骤
- 识别用户群体：产品可能服务多类用户，先列出所有用户类型
- 选择主要用户：选择2-3个最重要的用户类型深入分析
- 收集用户数据：
- 如果有现有用户：分析用户数据、用户访谈、用户调研
- 如果是新产品：竞品用户分析、目标市场研究、假设验证
- 具象化描述：给用户起名字、配图片，让用户"活"起来
- 验证画像：与真实用户对比，确保画像准确
多用户画像的处理
如果产品服务多类用户，需要：
- 明确主要用户（Primary Persona）：产品主要服务的用户
- 识别次要用户（Secondary Persona）：也会使用但不是核心
- 考虑边缘用户（Edge Persona）：极端情况下的用户
优先级原则：
- 主要用户的需求优先满足
- 次要用户的需求在不影响主要用户的前提下满足
- 边缘用户的需求用于测试产品的健壮性
2. 用户旅程图 (User Journey Map)
用户旅程图描绘用户完成核心任务的完整路径，包括每个阶段的行为、想法、情绪和痛点。
用户旅程图模板
journey
title 用户完成核心任务的旅程
section 发现阶段
了解产品: 3: 用户
产生兴趣: 4: 用户
section 使用阶段
首次使用: 3: 用户
完成任务: 5: 用户
section 留存阶段
持续使用: 4: 用户
推荐他人: 5: 用户
旅程图的关键要素
1. 阶段划分
典型的用户旅程包含以下阶段：
- 认知阶段：用户如何知道产品
- 考虑阶段：用户如何评估产品
- 购买/注册阶段：用户如何开始使用
- 首次使用阶段：用户第一次使用的体验
- 持续使用阶段：用户日常使用的体验
- 推荐阶段：用户是否会推荐给他人
2. 每个阶段的分析维度
| 维度 | 描述 |
|---|---|
| 行为 | 用户在做什么 |
| 想法 | 用户在想什么 |
| 情绪 | 用户的情绪状态（1-5分） |
| 触点 | 用户与产品的接触点 |
| 痛点 | 用户遇到的问题 |
| 机会 | 我们可以改进的地方 |
3. 情绪曲线
用1-5分标注用户在每个阶段的情绪：
- 5分：非常满意，"Wow"体验
- 4分：满意
- 3分：一般，没有特别感受
- 2分：不满意，有些失望
- 1分：非常不满意，想放弃
目标：
- 识别情绪低点（痛点），优先优化
- 创造情绪高点（峰值体验），形成记忆点
- 确保结束时情绪高（峰终定律）
创建用户旅程图的步骤
- 定义核心任务：用户使用产品要完成的主要任务
- 划分旅程阶段：将任务分解为关键阶段
- 填充每个阶段：
- 用户在做什么（行为）
- 用户在想什么（想法）
- 用户的情绪如何（情绪）
- 用户遇到什么问题（痛点）
- 绘制情绪曲线：可视化用户的情绪变化
- 识别优化机会：在痛点处寻找改进机会
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.
- 24d ago Changed · +2 lines 596aac9cb7c9
- 1mo ago First seen · 245 lines · 28 tokens per session scan A f999a8dc21b9
pm/user-research is a skill published in the GitHub repository echoVic/boss-skill (543 stars, last pushed 6d ago), licensed MIT. It adds 28 tokens to every session and 2,144 once invoked, about $0.0001 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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