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-roi-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-roi-analysis)<a href="https://agentmods.dev/skills/guofu-shiqu/ux-expert-skills/exp-roi-analysis"><img src="https://agentmods.dev/badge/skills/guofu-shiqu/ux-expert-skills/exp-roi-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-roi-analysis"><img src="https://agentmods.dev/badge/skills/guofu-shiqu/ux-expert-skills/exp-roi-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 | $0.00039 | $0.01752 |
| Opus 5 | $0.00019 | $0.00876 |
| Sonnet 5 | $0.00008 | $0.00350 |
| Haiku 4.5 | $0.00004 | $0.00175 |
Grade A, and why
exp-roi-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.
How it starts
The opening of the file, as written. The whole thing — 196 lines — stays where its author put it; the contents beside it link to each section on GitHub.
体验ROI分析
评估体验投入的商业回报，量化体验对业务指标的影响，建立体验投资与业务成果的关联模型。
触发条件
- 需要评估体验投入的商业回报
- 需要量化体验对业务指标的影响
- 需要向管理层汇报体验价值
- 需要为体验优化项目争取资源
- 需要建立体验ROI监测体系
核心能力
1. 体验价值传导模型
建立"体验优化 → 用户行为变化 → 业务指标变化 → 财务影响"的价值传导链：
体验优化
↓
用户行为变化：转化率↑ / 留存率↑ / 使用频率↑ / 负面评价↓
↓
业务指标变化：收入↑ / 成本↓ / 口碑传播↑
↓
财务影响：LTV（用户生命周期价值）↑ / CAC（获客成本）↓ / 运营成本↓
2. 体验ROI计算方法
方法一：LOE（Lost Opportunity Estimate）法
体验损失机会成本 = 受影响用户数 × 用户价值 × 流失率影响
示例：
注册流程体验差导致 5% 用户流失
受影响用户数：10,000人/月
人均 LTV：¥500
体验损失成本：10,000 × 5% × ¥500 = ¥250,000/月
方法二：NPS 收入影响法
NPS 每提升 1 分对收入的影响 = 历史数据回归分析
一般规律（互联网行业）：
NPS 提升 7-10 分 → 收入增长约 1%
NPS 持续 >50 → 自然增长显著高于行业平均
方法三：体验改善A/B测试法
对照组（未改善）转化率：[X%]
实验组（已改善）转化率：[Y%]
提升：[Y-X] 个百分点
月度增量收入 = 月活用户 × [Y-X]% × 人均月收入
年度ROI = 年度增量收入 ÷ 体验改善投入成本
方法四：客户服务质量成本法
服务体验差导致的成本：
= 投诉处理成本 + 用户流失损失 + 口碑修复成本 + 客服增量成本
体验改善节省成本 = 改善前成本 - 改善后成本
3. 关键指标体系
体验指标 → 行为指标 → 业务指标 三级关联：
| 体验指标 | 影响的行为指标 | 影响的业务指标 |
|---|---|---|
| CSAT 提升 | 复购率↑、使用频率↑ | 收入↑、LTV↑ |
| NPS 提升 | 推荐率↑、新用户获取成本↓ | 自然增长↑、CAC↓ |
| CES 降低 | 任务完成率↑、客服求助↓ | 转化↑、服务成本↓ |
| 错误率降低 | 用户流失↓、负面评价↓ | 品牌口碑↑、挽回成本↓ |
4. ROI 报告框架
完整ROI分析报告包含：
- 背景与目标 — 为什么要做这个体验项目
- 投入成本 — 人力、时间、工具、机会成本
- 体验改善 — 具体改善了什么体验指标
- 行为变化 — 用户行为发生了什么变化
- 业务影响 — 业务指标有什么变化
- 财务计算 — ROI 具体数值
- 风险提示 — 计算假设和不确定性
- 后续建议 — 基于ROI结果的下一步
5. 体验ROI基准参考
| 行业 | 体验改善ROI中位值 | 说明 |
|---|---|---|
| SaaS | 3-5X | 留存提升带来高LTV增长 |
| 电商 | 2-4X | 复购和客单价提升 |
| 金融 | 4-7X | 信任改善带来高价值转化 |
| 出行/生活服务 | 2-3X | 频率提升带来规模效应 |
输出格式：体验ROI分析报告
【体验ROI分析报告】
▸ 项目背景：
项目名称：[...]
改善目标：[...]
改善时间：[...]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
▸ 一、投入成本
人力成本：[...] 人天 × ¥[...] = ¥[...]
工具/技术成本：¥[...]
机会成本：¥[...]（因做此项目放弃的其他机会）
─────────────────────
总投入成本：¥[...]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
▸ 二、体验改善效果
体验指标 │ 改善前 │ 改善后 │ 变化
────────┼───────┼───────┼──────
CSAT │ XX% │ XX% │ ↑ X.X%
NPS │ XX │ XX │ ↑ X分
CES │ X.XX │ X.XX │ ↓ X.XX
任务完成率 │ XX% │ XX% │ ↑ X.X%
错误率 │ XX% │ XX% │ ↓ X.X%
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
▸ 三、业务影响
业务指标 │ 改善前 │ 改善后 │ 变化
──────────┼──────────┼──────────┼──────
转化率 │ XX% │ XX% │ ↑ X.X%
留存率（次月） │ XX% │ XX% │ ↑ X.X%
人均收入 │ ¥XXX │ ¥XXX │ ↑ ¥XX
获客成本CAC │ ¥XXX │ ¥XXX │ ↓ ¥XX
服务成本 │ ¥XXX/人 │ ¥XXX/人 │ ↓ ¥XX
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
▸ 四、财务计算
计算方法：[LOE法 / NPS法 / A-B测试法 / 成本法]
月度增量收入：¥[...]
年度增量收入：¥[...]
年度节省成本：¥[...]
─────────────────────
年度总收益：¥[...]
ROI = 年度总收益 ÷ 总投入成本 = [X.X]X
回本周期：[X] 个月
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
▸ 五、风险提示
计算假设：
[假设1]：置信度 [高/中/低]
[假设2]：置信度 [高/中/低]
不确定性因素：
[因素1]：可能影响 [±X%]
[因素2]：可能影响 [±X%]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
▸ 六、后续建议
基于ROI结果的下一步：
1. [...]
2. [...]
3. [...]
可复制到其他场景：
[场景1]：预计ROI [...]
[场景2]：预计ROI [...]
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 · 196 lines · 39 tokens per session scan A 5bbbae16f376
exp-roi-analysis is a skill published in the GitHub repository guofu-shiqu/ux-expert-skills (2 stars, last pushed 2mo ago), licensed MIT. It adds 39 tokens to every session and 1,752 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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