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-scenario-marketinggit 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-scenario-marketing)<a href="https://agentmods.dev/skills/guofu-shiqu/ux-expert-skills/exp-scenario-marketing"><img src="https://agentmods.dev/badge/skills/guofu-shiqu/ux-expert-skills/exp-scenario-marketing/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-scenario-marketing"><img src="https://agentmods.dev/badge/skills/guofu-shiqu/ux-expert-skills/exp-scenario-marketing.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 | $0.00729 |
| Opus 5 | $0.00020 | $0.00365 |
| Sonnet 5 | $0.00008 | $0.00146 |
| Haiku 4.5 | $0.00004 | $0.00073 |
Grade A, and why
exp-scenario-marketing 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
场景化体验营销设计
基于用户场景中的任务、情绪和决策阻力，设计触达时机、内容、体验动作和转化路径。
触发条件
- 需要在特定场景中触达用户
- 需要推动转化
- 需要增强留存
- 需要挽回流失
- 需要设计场景化的营销干预
核心能力
1. 识别营销类型
根据场景和目标，选择适合的营销类型：
| 营销类型 | 适用场景 | 核心目标 |
|---|---|---|
| 认知型 | 用户还未意识到需求 | 唤醒需求、建立认知 |
| 决策型 | 用户在决策关头犹豫 | 降低决策阻力、推动转化 |
| 使用型 | 用户正在使用产品 | 提升体验、增强粘性 |
| 关系型 | 用户已成客户 | 深化关系、提升忠诚度 |
| 挽回型 | 用户已流失或即将流失 | 重新激活、挽回流失 |
2. 分析场景要素
- 目标场景：用户处于什么场景
- 用户 JTBD：用户想要完成什么任务
- 情绪状态：用户当前的情绪是什么
- 决策阻力：是什么阻碍了用户行动
3. 设计触达策略
- 触达时机：在什么时刻触达最有效
- 触达内容：传达什么信息最能打动用户
- 体验动作：做什么体验动作能提升效果
- 转化路径：如何让用户顺畅完成转化
4. 设计品牌印记
确保营销动作能留下正面的品牌记忆。
输出格式：场景化体验营销分析卡
【场景化体验营销分析卡】
▸ 目标场景：[...]
▸ 用户 JTBD：
功能任务：[帮我...]
情绪任务：[让我感到...]
社会任务：[让我看起来...]
▸ 情绪状态：[...]
▸ 决策阻力：[...]
▸ 营销类型：[认知型/决策型/使用型/关系型/挽回型]
▸ 触达时机：[...]
▸ 触达内容：
[传达的核心信息和表达方式和文案示例]
▸ 体验动作：
[做的体验动作，不限于营销本身，包括产品内的体验优化]
▸ 转化路径：
[用户从触达到完成转化的顺畅路径]
▸ 品牌印记：
[用户会记住什么关于品牌的信息]
▸ 衡量指标：
[可以衡量这个营销效果的数据指标]
使用方法
当需要在特定场景中设计营销干预时，调用本 skill 进行系统性的场景分析和营销策略设计。
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 · 85 lines · 40 tokens per session scan A a12f47bcc171
exp-scenario-marketing 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 729 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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