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 guofu-shiqu/ux-expert-skills --skill exp-persona-buildinggit clone --depth 1 https://github.com/guofu-shiqu/ux-expert-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/guofu-shiqu/ux-expert-skills/exp-persona-building)<a href="https://agentmods.dev/skills/guofu-shiqu/ux-expert-skills/exp-persona-building"><img src="https://agentmods.dev/badge/skills/guofu-shiqu/ux-expert-skills/exp-persona-building/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/guofu-shiqu/ux-expert-skills/exp-persona-building"><img src="https://agentmods.dev/badge/skills/guofu-shiqu/ux-expert-skills/exp-persona-building.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 | $0.00033 | $0.01263 |
| Opus 5 | $0.00016 | $0.00632 |
| Sonnet 5 | $0.00007 | $0.00253 |
| Haiku 4.5 | $0.00003 | $0.00126 |
Grade A, and why
exp-persona-building 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 12d 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
用户画像构建
基于用户研究数据（访谈、问卷、行为数据、VOC 等），构建结构化的用户画像（Persona），为产品设计和体验决策提供共享的用户认知基础。
触发条件
- 需要构建用户画像指导设计决策
- 团队对目标用户认知不一致
- 需要将零散的用户研究数据整合为可共情的画像
- 需要为不同用户群体设计差异化体验
- 产品初期需要明确目标用户
核心能力
1. 画像类型选择
根据目标选择合适的画像类型：
| 画像类型 | 数据基础 | 适用场景 | 精度 |
|---|---|---|---|
| 假设画像 | 团队经验推断 | 产品 0-1 阶段、快速验证 | 低 |
| 质性画像 | 用户访谈、观察 | 深度理解特定用户群 | 中 |
| 数据驱动画像 | 行为数据+问卷+访谈 | 成熟产品精细化运营 | 高 |
| 统计聚类画像 | 大规模行为数据聚类 | 用户分群、个性化推荐 | 高 |
2. 画像构建维度
从以下维度构建完整画像：
基础属性
- 人口统计：年龄、性别、地域、教育、职业、收入
- 设备特征：常用设备、系统、网络环境
- 使用频率：日活/周活/月活
行为特征
- 核心场景：在什么场景下使用产品
- 任务频率：高频/中频/低频任务
- 功能偏好：常用功能、忽略功能
- 行业路径：用户进入产品的典型路径
心理特征
- 目标与动机：用户想要完成什么（链接 JTBD）
- 态度与信念：对产品/品类的态度
- 痛点与挫折：当前最大的困扰
- 价值观：影响决策的核心价值观
能力特征
- 数字素养：技术熟悉程度
- 领域知识：对产品领域的了解程度
- 学习方式：偏好看/听/做
3. 画像构建流程
- 数据收集 — 整合访谈、问卷、行为数据、VOC
- 模式识别 — 从数据中识别用户群体的共性模式
- 聚类分组 — 将相似用户归为一组
- 优先级排序 — 识别最重要的 3-5 个画像
- 画像细化 — 为每个画像填充完整维度
- 场景化 — 为画像添加典型使用场景
- 验证校准 — 与实际数据交叉验证
4. 画像使用原则
- 画像数量控制在 3-5 个，过多则失去聚焦
- 画像应基于数据而非猜测
- 画像应包含"一天的典型使用"使其鲜活
- 画像需定期更新（至少每年一次）
- 画像应成为团队共享语言
输出格式：用户画像卡
【用户画像卡】
▸ 画像名称：[如"效率型管理者-张明"]
▸ 画像类型：[假设/质性/数据驱动/统计聚类]
▸ 优先级：[核心画像 / 次要画像 / 边缘画像]
▸ 代表用户占比：[XX%]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
▸ 基础属性
年龄：[...] 性别：[...] 地域：[...]
职业：[...] 收入：[...] 教育：[...]
常用设备：[...] 使用频率：[...]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
▸ 行为特征
核心场景：[...]
高频任务：[...]
低频任务：[...]
功能偏好：[...]
典型路径：[...]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
▸ 心理特征
目标与动机：
功能目标：[帮我...]
情绪目标：[让我感到...]
社会目标：[让我看起来...]
态度与信念：[...]
核心痛点：[...]
价值观：[...]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
▸ 能力特征
数字素养：[高/中/低]
领域知识：[深/中/浅]
学习方式：[看/听/做]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
▸ 典型一天
[用叙事方式描述这个画像用户典型的一天中如何与产品交互]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
▸ 体验需求
1. [需求1]
2. [需求2]
3. [需求3]
▸ 设计启示
1. [启示1]
2. [启示2]
3. [启示3]
▸ 数据来源
[画像基于什么数据构建]
[最后更新时间]
使用方法
当需要构建或更新用户画像时调用本 skill。画像构建后可与场景识别、旅程分析、策略生成等 skill 组合使用，确保所有体验决策都基于对用户的准确理解。
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.
- 12d ago First seen · 145 lines · 33 tokens per session scan A dca18cd3a357
exp-persona-building is a skill published in the GitHub repository guofu-shiqu/ux-expert-skills (2 stars, last pushed 2mo ago), licensed MIT. It adds 33 tokens to every session and 1,263 once invoked, about $0.0002 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 2026-08-31.
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