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-scene-recognitiongit 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-scene-recognition)<a href="https://agentmods.dev/skills/guofu-shiqu/ux-expert-skills/exp-scene-recognition"><img src="https://agentmods.dev/badge/skills/guofu-shiqu/ux-expert-skills/exp-scene-recognition/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-scene-recognition"><img src="https://agentmods.dev/badge/skills/guofu-shiqu/ux-expert-skills/exp-scene-recognition.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 | $0.00049 | $0.00615 |
| Opus 5 | $0.00024 | $0.00308 |
| Sonnet 5 | $0.00010 | $0.00123 |
| Haiku 4.5 | $0.00005 | $0.00061 |
Grade A, and why
exp-scene-recognition 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
体验场景识别与还原
从碎片化信息中识别用户处于什么场景、触发条件是什么、情绪状态如何，并生成结构化场景卡。
触发条件
- 接收到用户反馈、服务录音、投诉或行为数据
- 需要还原体验现场
- 需要理解用户当下的情境和情绪
核心能力
从以下维度还原体验场景：
- 场景类型识别 — 判断是初次使用、日常使用、异常情境、切换任务、寻求帮助等哪种场景
- 体验对象识别 — 明确用户正在与哪个产品/服务/触点交互
- 触发条件分析 — 什么事件/状态导致用户进入这个场景
- 用户目标梳理 — 用户在这个场景下想要完成什么
- 当前阻力识别 — 什么阻碍了用户达成目标
- 情绪状态判断 — 用户在场景中的情绪是困惑、焦虑、满意、失望还是愤怒
- 业务影响评估 — 这个场景对业务指标（转化、留存、口碑）有何影响
- 体验机会发现 — 这个场景中存在的体验改善机会
输出格式：场景卡
【场景卡】
▸ 场景类型：[初次使用/日常使用/异常情境/切换任务/寻求帮助/...]
▸ 体验对象：[产品/服务/触点名称]
▸ 触发条件：[导致用户进入该场景的事件或状态]
▸ 用户目标：[用户想要完成什么]
▸ 当前阻力：[阻碍用户达成目标的因素]
▸ 情绪状态：[困惑/焦虑/满意/失望/愤怒/...]，强度：[低/中/高]
▸ 业务影响：[对转化/留存/口碑等指标的影响]
▸ 体验机会：[可以改善的具体机会点]
▸ 指标观察：[可以观察这个场景的相关指标]
▸ 可复用规则：[从这个场景中可以提炼的通用规则]
使用方法
当用户提供体验相关的原始信息（反馈、录音、数据等）时，调用本 skill 生成结构化场景卡，为后续的 JTBD 分析、旅程分析或策略生成提供基础。
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 · 49 lines · 49 tokens per session scan A d661e3b3c1fe
exp-scene-recognition is a skill published in the GitHub repository guofu-shiqu/ux-expert-skills (2 stars, last pushed 2mo ago), licensed MIT. It adds 49 tokens to every session and 615 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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