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-journey-analysisgit 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-journey-analysis)<a href="https://agentmods.dev/skills/guofu-shiqu/ux-expert-skills/exp-journey-analysis"><img src="https://agentmods.dev/badge/skills/guofu-shiqu/ux-expert-skills/exp-journey-analysis/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-journey-analysis"><img src="https://agentmods.dev/badge/skills/guofu-shiqu/ux-expert-skills/exp-journey-analysis.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 | $0.00040 | $0.00782 |
| Opus 5 | $0.00020 | $0.00391 |
| Sonnet 5 | $0.00008 | $0.00156 |
| Haiku 4.5 | $0.00004 | $0.00078 |
Grade A, and why
exp-journey-analysis 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
旅程还原与分析
先定义旅程尺度，基于证据还原实际旅程，再设计理想旅程，最后输出差距分析、关键断点和改善机会。
触发条件
- 需要理解一段完整的用户体验过程
- 需要诊断体验断点
- 需要对比实际与理想差距
- 需要识别旅程中的峰值和低谷
核心能力
1. 定义旅程尺度
先明确旅程的尺度和边界：
| 尺度 | 时间范围 | 适用场景 |
|---|---|---|
| 微型旅程 | 单次操作（几秒到几分钟） | 分析具体交互流程 |
| 小型旅程 | 单次使用会话（几分钟到几十分钟） | 分析一次完整的使用过程 |
| 中型旅程 | 跨天或跨周的多步流程 | 分析注册、购买等跨时段流程 |
| 大型旅程 | 跨月或跨季度的持续使用 | 分析用户生命周期 |
| 生命周期旅程 | 从认识到离开的完整过程 | 分析用户全生命周期体验 |
2. 还原实际旅程
基于证据（用户反馈、行为数据、服务记录等）还原用户实际经历的旅程：
- 列出所有实际发生的阶段
- 记录每个阶段用户的行为、目标、情绪
- 标注关键触点和交互
- 记录实际耗时和转化情况
3. 设计理想旅程
基于用户目标和业务目标，设计理想的旅程：
- 移除不必要的步骤
- 优化关键触点
- 设计顺畅的过渡
- 创造峰值体验
4. 差距分析
对比实际旅程和理想旅程，识别：
- 关键断点（用户流失、放弃、投诉的位置）
- 体验低谷（情绪曲线中的低点）
- 峰值机会（可以创造惊喜的时刻）
- 不必要的摩擦点
输出格式：旅程分析报告
【旅程分析报告】
▸ 旅程尺度：[微型/小型/中型/大型/生命周期]
▸ 边界定义：开始於[...]，结束于[...]
▸ 实际旅程阶段表：
阶段1：[名称]，行为：[...]，目标：[...]，情绪：[...]，触点：[...]
阶段2：...
...
▸ 理想旅程阶段表：
阶段1：[名称]，优化点：[...]
阶段2：...
...
▸ 差距分析：
关键断点：[...]
体验低谷：[...]
峰值机会：[...]
不必要摩擦：[...]
▸ 改善机会：
1. [...]
2. [...]
...
▸ 指标验证：
[可以观察这个旅程的相关指标]
▸ 资产沉淀：
[可以沉淀为 reusable asset 的内容]
使用方法
当需要分析用户完整体验过程时，调用本 skill 进行旅程还原和分析，识别断点和改善机会。
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.
- 12d ago First seen · 97 lines · 40 tokens per session scan A 416d114bdc6f
exp-journey-analysis is a skill published in the GitHub repository guofu-shiqu/ux-expert-skills (2 stars, last pushed 2mo ago), licensed MIT. It adds 40 tokens to every session and 782 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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