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-voc-insightgit 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-voc-insight)<a href="https://agentmods.dev/skills/guofu-shiqu/ux-expert-skills/exp-voc-insight"><img src="https://agentmods.dev/badge/skills/guofu-shiqu/ux-expert-skills/exp-voc-insight/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-voc-insight"><img src="https://agentmods.dev/badge/skills/guofu-shiqu/ux-expert-skills/exp-voc-insight.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 | $0.00042 | $0.00784 |
| Opus 5 | $0.00021 | $0.00392 |
| Sonnet 5 | $0.00008 | $0.00157 |
| Haiku 4.5 | $0.00004 | $0.00078 |
Grade A, and why
exp-voc-insight 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
VOC 洞察分析
对 VOC（客户原声）进行分类、情绪分析、痛点识别和需求挖掘，输出优先级排序和策略机会。
触发条件
- 接收到批量客户原声
- 接收到客服对话记录
- 接收到投诉记录
- 接收到 App 评价或社媒评论
- 需要提炼洞察和指导策略
核心能力
1. 分类和标签
对 VOC 进行分类：
- 按产品线/功能模块分类
- 按问题类型分类（功能缺陷、体验问题、需求建议、投诉等）
- 按用户类型分类（新用户、老用户、流失用户等）
2. 情绪强度评估
评估每条 VOC 的情绪强度：
- 强烈负面情绪（愤怒、失望）
- 中度负面情绪（不满、困惑）
- 中性表达
- 正面情绪（满意、惊喜）
3. 高频问题识别
识别高频问题和共性痛点：
- 统计问题出现频次
- 识别问题聚类
- 发现系统性问题
4. 深层需求挖掘
从表面表达中挖掘深层需求：
- 用户真正想要的是什么
- 背后的 JTBD 是什么
- 未被满足的潜在需求
5. 场景匹配
将 VOC 匹配到具体场景：
- 用户是在什么场景下遇到这个问题的
- 这个问题影响了哪些旅程阶段
输出格式：VOC 洞察分析报告
【VOC 洞察分析报告】
▸ 数据范围：
来源：[...]
时间范围：[...]
样本量：[...]
▸ 高频分类：
1. [问题类别]，频次：[...]，占比：[...%]
2. [...]
...
▸ 代表性表达：
"[直接引用用户原话]"
"[直接引用用户原话]"
...
▸ 情绪强度分布：
强烈负面：[...%]
中度负面：[...%]
中性：[...%]
正面：[...%]
▸ 体验断点（按频次排序）：
1. [断点描述]，影响用户数：[...]
2. [...]
...
▸ 对应场景：
[将体验断点映射到具体场景]
▸ JTBD 提炼：
[从 VOC 中提炼的 JTBD]
▸ 深层需求：
[从 VOC 中挖掘的深层需求]
▸ 优先级排序：
1. [问题]，优先级：[高/中/低]，理由：[...]
2. [...]
...
▸ 策略机会：
1. [策略方向]，预期效果：[...]
2. [...]
...
▸ 可自动化任务：
[哪些任务可以通过 Agent 自动处理]
▸ 验证指标：
[如何验证改善效果]
使用方法
当接收到批量 VOC 数据时，调用本 skill 进行系统性的洞察分析，输出优先级排序和可执行的策略机会。
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 · 115 lines · 42 tokens per session scan A e5985dfd49f1
exp-voc-insight is a skill published in the GitHub repository guofu-shiqu/ux-expert-skills (2 stars, last pushed 2mo ago), licensed MIT. It adds 42 tokens to every session and 784 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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