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-survey-designgit 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-survey-design)<a href="https://agentmods.dev/skills/guofu-shiqu/ux-expert-skills/exp-survey-design"><img src="https://agentmods.dev/badge/skills/guofu-shiqu/ux-expert-skills/exp-survey-design/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-survey-design"><img src="https://agentmods.dev/badge/skills/guofu-shiqu/ux-expert-skills/exp-survey-design.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 | $0.00041 | $0.01749 |
| Opus 5 | $0.00020 | $0.00874 |
| Sonnet 5 | $0.00008 | $0.00350 |
| Haiku 4.5 | $0.00004 | $0.00175 |
Grade A, and why
exp-survey-design 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 — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
体验问卷设计
基于体验测量目标，设计结构化的体验问卷，包括指标选择、题型设计、量表选择、问卷结构编排和投放策略。
触发条件
- 需要设计用户体验测量问卷
- 需要选择合适的量表和题型
- 需要制定问卷投放策略
- 需要编写具体的问卷题目
- 需要评估问卷质量
核心能力
1. 明确测量目标
在设计问卷前，先明确：
- 测量什么 — CSAT（满意度）、NPS（净推荐值）、CES（费力度）、自定义体验指标
- 测量谁 — 目标用户群体（全量/分层/特定场景用户）
- 测量何时 — 触发时机（即时/周期性/事件后）
- 测量为什么 — 结果用途（诊断/追踪/对比/预警）
2. 量表选择
根据测量目标选择合适的量表类型：
| 量表类型 | 适用场景 | 题目形式 | 量表范围 |
|---|---|---|---|
| CSAT 满意度量表 | 测量特定任务/触点的满意度 | "您对XX的满意度是？" | 1-5 分（非常不满意→非常满意） |
| NPS 净推荐值 | 测量整体忠诚度和口碑意愿 | "您向朋友推荐XX的可能性是？" | 0-10 分 |
| CES 费力度量表 | 测量任务完成费力度 | "您完成XX的费力程度是？" | 1-5 或 1-7 分 |
| CSATG 情绪满意度 | 测量情感层面的满意度 | 表情/情绪选择 | 5 档表情 |
| 自定义 Likert 量表 | 测量特定维度的认同度 | "您是否同意以下说法" | 5/7 点 Likert |
| 开放性问题 | 收集用户原声和深度反馈 | "您还有什么想说的？" | 文本输入 |
| 行为性问题 | 了解用户实际行为 | "您过去XX天内使用过几次？" | 选择题/填空 |
3. 题型设计
根据测量目标设计合适的题型：
封闭式题型：
- 单选题 — 用于明确选项的场景
- 多选题 — 用于多维度了解
- 量表题 — 用于态度/满意度测量
- 排序题 — 用于了解优先级
- 矩阵题 — 用于多维度对比
开放式题型：
- 文本题 — 用于收集用户原声
- 长文本 — 用于深度反馈
- 语音输入 — 用于降低填写门槛
行为追踪题型：
- 频次题 — "您过去X天使用过几次？"
- 时长题 — "您每次使用多长时间？"
- 路径题 — "您通常通过什么方式进入？"
4. 问卷结构编排
遵循以下原则编排问卷结构：
- 开场白 — 简短说明目的、时长、隐私保护
- 筛选题 — 确保受访者是目标用户
- 核心量表题 — CSAT/NPS/CES 等核心指标（放在前面避免疲劳偏差）
- 维度诊断题 — 针对特定体验维度的诊断问题
- 行为题 — 了解用户行为背景
- 开放题 — 收集深度反馈（放在后面）
- 结束语 — 感谢语和可选的后续跟进
5. 投放策略
根据场景选择合适的投放时机和渠道：
| 投放时机 | 适用场景 | 优势 | 劣势 |
|---|---|---|---|
| 任务完成后即时 | 测量特定任务体验 | 记忆鲜活、率高 | 可能打扰用户 |
| 会话结束时 | 测量整体使用体验 | 自然过渡 | 回收率中等 |
| 事件触发后 | 测量特定事件体验 | 精准对应场景 | 需要事件追踪 |
| 周期性推送 | 追踪体验趋势 | 可纵向对比 | 回收率递减 |
| 弹窗邀请 | 获取广泛样本 | 覆盖面广 | 可能打扰用户 |
| 邮件/短信 | 深度调研 | 可放长问卷 | 回收率低 |
6. 问卷质量检查
设计完成后，检查以下质量要点：
- 题目数量 — 核心问卷不超过 5-7 题，深度调研不超过 15-20 题
- 题目表述 — 避免引导性、歧义性、双重含义
- 量表一致性 — 同一问卷中量表方向和刻度保持一致
- 跳转逻辑 — 确保跳转合理，不出现死循环或遗漏
- 填写时长 — 核心问卷控制在 1 分钟内，深度调研控制在 5 分钟内
输出格式：问卷设计方案
【问卷设计方案】
▸ 测量目标：
测量指标：[CSAT/NPS/CES/自定义]
目标用户：[...]
触发时机：[...]
结果用途：[诊断/追踪/对比/预警]
▸ 问卷结构：
1. 开场白：[...]
2. 筛选题：[...]
3. 核心量表题：[...]
4. 维度诊断题：[...]
5. 行为题：[...]
6. 开放题：[...]
7. 结束语：[...]
▸ 题目详情：
Q1 [量表题/CSAT]：
题目：[...]
选项：[1-5 分量表]
说明：[...]
Q2 [量表题/NPS]：
题目：[...]
选项：[0-10 分量表]
说明：[...]
Q3 [单选题]：
题目：[...]
选项：[A/B/C/D]
跳转逻辑：[...]
Q4 [开放题]：
题目：[...]
输入方式：[文本/语音]
...
▸ 投放策略：
投放渠道：[App内/邮件/短信/弹窗]
投放时机：[...]
投放频率：[...]
预估回收率：[...]
▸ 质量检查：
题目数量：[...]
预估填写时长：[...分钟]
量表一致性：[是/否]
跳转逻辑检查：[通过/有问题]
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 · 155 lines · 41 tokens per session scan A e959fcf201cb
exp-survey-design is a skill published in the GitHub repository guofu-shiqu/ux-expert-skills (2 stars, last pushed 2mo ago), licensed MIT. It adds 41 tokens to every session and 1,749 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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