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-strategy-generationgit 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-strategy-generation)<a href="https://agentmods.dev/skills/guofu-shiqu/ux-expert-skills/exp-strategy-generation"><img src="https://agentmods.dev/badge/skills/guofu-shiqu/ux-expert-skills/exp-strategy-generation/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-strategy-generation"><img src="https://agentmods.dev/badge/skills/guofu-shiqu/ux-expert-skills/exp-strategy-generation.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 | $0.00035 | $0.00772 |
| Opus 5 | $0.00017 | $0.00386 |
| Sonnet 5 | $0.00007 | $0.00154 |
| Haiku 4.5 | $0.00003 | $0.00077 |
Grade A, and why
exp-strategy-generation 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
体验策略生成
遵循"证据→场景→JTBD→旅程→断点→洞察→机会点→策略→动作→指标→复盘→资产回写"路径，从六个维度生成策略。
触发条件
- 已完成场景还原、旅程分析和洞察提炼
- 需要生成可执行的体验改善策略
- 需要从多个维度系统性地提出策略
- 需要确保策略有证据支撑和可衡量效果
核心能力
策略生成路径
遵循以下路径生成策略：
证据 → 场景 → JTBD → 旅程 → 断点 → 洞察 → 机会点 → 策略 → 动作 → 指标 → 复盘 → 资产回写
六个策略维度
从以下六个维度生成体验改善策略：
| 维度 | 说明 | 策略方向 |
|---|---|---|
| 1. 产品维度 | 产品功能、交互、信息架构 | 功能优化、交互改善、信息重构 |
| 2. 服务维度 | 服务交付、员工支持、后台流程 | 服务流程优化、员工赋能、系统支持 |
| 3. 运营维度 | 用户运营、活动策划、触达策略 | 用户分层运营、场景化营销、留存策略 |
| 4. 品牌维度 | 品牌认知、品牌体验、品牌记忆 | 品牌印记设计、体验一致性、品牌传播 |
| 5. 员工体验维度 | 员工能力、工具、情绪、协作 | 员工支持、培训赋能、情绪管理 |
| 6. Agent 维度 | 自动化、智能化、个性化 | Agent 可执行动作、自动化流程、智能推荐 |
输出格式：完整体验策略报告
【完整体验策略报告】
▸ 任务背景：[...]
▸ 核心对象：[...]
▸ 旅程尺度：[微型/小型/中型/大型/生命周期]
▸ 关键场景：[...]
▸ JTBD：
功能任务：[帮我...]
情绪任务：[让我感到...]
社会任务：[让我看起来...]
▸ 实际旅程：[...]
▸ 理想旅程：[...]
▸ 断点：[...]
▸ 认知阻力：[...]
▸ 峰值低谷：[...]
▸ 场景化营销机会：[...]
▸ 六维策略：
1. 产品维度：[...]
2. 服务维度：[...]
3. 运营维度：[...]
4. 品牌维度：[...]
5. 员工体验维度：[...]
6. Agent 维度：[...]
▸ Agent 可执行动作：
[哪些动作可以通过 Agent 自动执行]
▸ 指标验证：
[如何衡量策略效果]
▸ 资产回写：
[哪些产出可以沉淀为 reusable asset]
使用方法
当已完成前期分析（场景还原、旅程分析、洞察提炼）后，调用本 skill 生成系统性的体验改善策略。
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 · 83 lines · 35 tokens per session scan A d26784c259ca
exp-strategy-generation is a skill published in the GitHub repository guofu-shiqu/ux-expert-skills (2 stars, last pushed 2mo ago), licensed MIT. It adds 35 tokens to every session and 772 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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