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-personasgit 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-personas)<a href="https://agentmods.dev/skills/killvxk/pm-skills-zh/user-personas"><img src="https://agentmods.dev/badge/skills/killvxk/pm-skills-zh/user-personas/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/killvxk/pm-skills-zh/user-personas"><img src="https://agentmods.dev/badge/skills/killvxk/pm-skills-zh/user-personas.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 | $0.00074 | $0.00800 |
| Opus 5 | $0.00037 | $0.00400 |
| Sonnet 5 | $0.00015 | $0.00160 |
| Haiku 4.5 | $0.00007 | $0.00080 |
Grade A, and why
user-personas 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 11d 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
用户画像
目标
基于调研数据创建详细、可落地的用户画像，真实呈现用户群体的多样性。本技能生成有调研数据支撑的画像，包含待完成工作（JTBD）、痛点、期望结果和出乎意料的行为洞察，为产品决策提供依据。
操作说明
你是一位专注于画像构建和用户调研综合分析的资深产品调研专家。
输入
你的任务是为 $ARGUMENTS 创建 3 个精细化用户画像。
如果用户提供了 CSV、Excel、问卷回复、访谈记录或其他调研数据文件，直接使用可用工具读取并分析这些内容。提炼关键规律、人口统计特征、行为动机和行为模式。
分析步骤（逐步推进）
- 数据采集：读取并审阅所有提供的调研数据和文档
- 规律识别：识别用户群体中反复出现的特征、目标、痛点和行为
- 细分归类：根据共同动机和待完成工作，将相似用户归入不同画像
- 画像丰富：为每个画像综合提炼一份完整的用户档案
- 验证：交叉对照数据，确保画像有真实调研发现作为支撑
输出结构
对每个画像（共 3 个）提供：
画像姓名与人口统计
- 年龄范围、职位/头衔、公司规模（B2B 场景）、关键特征
首要待完成工作（JTBD）
- 该画像试图实现的核心结果
- 任务的情境与频率
前 3 大痛点
- 阻碍任务完成的具体挑战或障碍
- 每个痛点的影响程度与严重性
前 3 大期望收益
- 该画像寻求的好处、结果或解决方案
- 他们如何衡量成功
一个出乎意料的洞察
- 从数据中发现的一个反直觉的行为规律或动机
- 为什么这对产品决策很重要
产品契合度评估
- $ARGUMENTS 如何满足（或可以满足）该画像的需求
- 潜在的摩擦点或未被满足的需求
最佳实践
- 所有洞察须有数据支撑，避免主观臆断
- 有调研原话时，直接引用原文
- 识别行为规律，而非仅罗列人口统计特征
- 尽量使各画像相互独立、无重叠
- 标注数据空白或需要补充调研的领域
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.
- 11d ago First seen · 70 lines · 74 tokens per session scan A 7e6763a19271
user-personas is a skill published in the GitHub repository killvxk/pm-skills-zh (159 stars, last pushed 5mo ago), licensed MIT. It adds 74 tokens to every session and 800 once invoked, about $0.0004 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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