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-service-reviewgit 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-service-review)<a href="https://agentmods.dev/skills/guofu-shiqu/ux-expert-skills/exp-service-review"><img src="https://agentmods.dev/badge/skills/guofu-shiqu/ux-expert-skills/exp-service-review/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-service-review"><img src="https://agentmods.dev/badge/skills/guofu-shiqu/ux-expert-skills/exp-service-review.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 | $0.00028 | $0.00761 |
| Opus 5 | $0.00014 | $0.00380 |
| Sonnet 5 | $0.00006 | $0.00152 |
| Haiku 4.5 | $0.00003 | $0.00076 |
Grade A, and why
exp-service-review 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
服务体验复盘
还原服务路径和关键触点表现，识别体验低谷和峰值机会，输出改善策略和预警机制。
触发条件
- 一次服务事件结束后
- 需要进行系统复盘
- 需要提炼经验教训
- 需要识别系统性问题
- 需要建立预警机制
核心能力
复盘框架
按照以下框架进行系统复盘：
- 事件概述 — 发生了什么，涉及哪些用户和服务
- 旅程尺度与边界 — 复盘的范围是什么
- 用户目标 — 用户想要完成什么
- 服务路径还原 — 实际发生了什么
- 关键触点表现 — 每个触点的表现如何
- 体验低谷识别 — 哪些地方体验不好
- 峰值机会识别 — 哪些地方可以创造峰值
- 员工体验问题 — 员工侧有什么问题
- 后台流程问题 — 后台有什么问题
- 品牌印记表现 — 用户记住了什么
- 改善策略 — 如何改善
- 指标与预警 — 如何监测和预警
- 资产回写 — 哪些经验可以沉淀
输出格式：服务体验复盘报告
【服务体验复盘报告】
▸ 事件概述：
事件名称：[...]
发生时间：[...]
涉及用户：[...]
涉及服务：[...]
▸ 旅程尺度与边界：
尺度：[微型/小型/中型/大型/生命周期]
开始於：[...]
结束于：[...]
▸ 用户目标：[用户想要完成什么]
▸ 服务路径还原：
阶段1：[行为]，触点：[...]，表现：[...]
阶段2：[...]
...
▸ 关键触点表现：
触点1：[名称]，表现：[好/中/差]，问题：[...]
触点2：[...]
...
▸ 体验低谷：
1. [低谷描述]，影响：[...]
2. [...]
...
▸ 峰值机会：
1. [可以创造峰值的时刻]，类型：[安心/效率/被理解/专业/惊喜/结尾]
2. [...]
...
▸ 员工体验问题：
[员工侧有什么问题]
▸ 后台流程问题：
[后台有什么问题]
▸ 品牌印记表现：
[用户记住了什么关于品牌的信息]
[是正面的还是负面的]
▸ 改善策略：
1. [...]
2. [...]
...
▸ 指标与预警：
监测指标：[...]
预警阈值：[...]
响应动作：[...]
▸ 资产回写：
[哪些经验可以沉淀为 reusable asset]
使用方法
当一次服务事件结束后，调用本 skill 进行系统复盘，还原服务路径，识别问题和机会，输出改善策略和预警机制。
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 · 103 lines · 28 tokens per session scan A f2990f678beb
exp-service-review is a skill published in the GitHub repository guofu-shiqu/ux-expert-skills (2 stars, last pushed 2mo ago), licensed MIT. It adds 28 tokens to every session and 761 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 2026-08-31.
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