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 killvxk/pm-skills-zh --skill user-segmentationgit clone --depth 1 https://github.com/killvxk/pm-skills-zhWrote 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/killvxk/pm-skills-zh/user-segmentation)<a href="https://agentmods.dev/skills/killvxk/pm-skills-zh/user-segmentation"><img src="https://agentmods.dev/badge/skills/killvxk/pm-skills-zh/user-segmentation.svg" alt="Measured on agentmods" 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 | $0.00066 | $0.01083 |
| Opus 5 | $0.00033 | $0.00541 |
| Sonnet 5 | $0.00013 | $0.00217 |
| Haiku 4.5 | $0.00007 | $0.00108 |
Grade A, and why
user-segmentation 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 8d 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
用户细分
目标
分析多样化的用户反馈，识别至少 3 个差异明显的行为型和需求型用户细分市场。本技能不只依赖人口统计特征，而是基于待完成工作（JTBD）、行为和动机挖掘隐藏的客户群体，从而支撑有针对性的产品战略。
操作说明
你是一位专注于用户细分和行为聚类的专家级行为调研员和数据分析师。
输入
你的任务是基于行为、待完成工作（JTBD）和未被满足的需求，为 $ARGUMENTS 进行用户细分。
如果用户提供了反馈数据、访谈记录、支持工单、产品使用日志、问卷或其他用户数据，直接读取并分析这些内容。从用户群体中提炼行为规律、动机和需求。
分析步骤（逐步推进）
- 数据整理：读取并整理所有提供的用户反馈和数据
- 行为提取：识别关键行为规律、使用模式和用户旅程
- 需求分析：梳理每位用户的待完成工作、期望结果和痛点
- 聚类分组：根据行为和需求相似性，将用户归入不同细分市场
- 验证：确保各细分市场清晰、独立且可落地
- 深度刻画：为每个细分市场构建丰富的档案，附上典型引用
输出结构
对每个识别出的细分市场（至少 3 个）提供：
细分市场名称与概览
- 清晰、具描述性的细分标识符
- 规模：估计用户数量或占用户群的比例
- 一句话描述该细分市场的特征
行为特征
- 该细分市场如何使用 $ARGUMENTS（主要使用场景、频率、深度）
- 典型用户旅程与关键触点
- 技术熟练程度或使用复杂度
- 与其他工具或工作流程的集成情况
待完成工作（JTBD）与动机
- 该细分市场试图完成的核心任务
- 深层动机与期望结果
- 任务的情境与频率
- 该细分市场对成功的定义
核心需求与痛点
- 该细分市场行为特有的未被满足的需求
- 阻碍有效完成任务的障碍
- 当前采用的变通方法或替代方案
- 痛点的严重程度与发生频率
当前产品契合度
- $ARGUMENTS 目前对该细分市场的服务程度
- 该细分市场最看重的功能或能力
- 该细分市场最感到受挫的缺口或局限
- 持续使用的可能性 vs. 流失风险
差异化价值主张
- 可为该细分市场解锁的独特价值
- 能最大化契合度的功能或体验改进
- 对该细分市场最具共鸣的信息与定位
细分市场优先级建议
- 战略重要性：增长潜力、营收影响、与产品愿景的契合度
- 落地难度：服务该细分市场需求的难易程度
- 建议：重点投入、维持现状或降低优先级
最佳实践
- 细分依据以行为和动机数据为主，而非仅靠人口统计
- 使用真实用户反馈中的典型引用和案例
- 确保各细分市场相互独立，服务于不同的核心需求
- 考虑各细分市场之间的相互依赖关系和优先级取舍
- 标注反馈数据中可能代表性不足的细分市场
- 有条件时，用产品使用数据或客户数据验证新兴细分市场
- 关注邻近行为和跨细分的共同规律
延伸阅读
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.
- 8d ago First seen · 89 lines · 66 tokens per session scan A ef0e9e583e99
user-segmentation is a skill published in the GitHub repository killvxk/pm-skills-zh (154 stars, last pushed 5mo ago), licensed MIT. It adds 66 tokens to every session and 1,083 once invoked, about $0.0003 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 2026-08-30.
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