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Agent Skill · 研究检索

user-modeling(用户建模)

user-modeling 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

user-modeling 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证为 MIT,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 47,500。

总安装 729周安装 31GitHub Stars 4下载 255核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:user-modeling(用户建模)
来源仓库:https://github.com/abhsin/designskills
仓库路径:skills/user-modeling
安装命令:
npx skills add https://github.com/abhsin/designskills --skill user-modeling
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/abhsin/designskills --skill user-modeling

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中快速定位候选结果。适用于根据关键词或任务场景进行信息检索和筛选的场景。通过 npx skills add 命令从指定 GitHub 仓库安装使用。安装前需确认权限范围和维护状态,注意可能触发联网或文件操作。

user-modeling 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/user-modeling

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/abhsin/designskills --skill user-modeling;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

user-modeling 怎么安装?

在终端执行 npx skills add https://github.com/abhsin/designskills --skill user-modeling。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

user-modeling 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

user-modeling 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

user-modeling 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

user-modeling 用的人多吗?

公开数据是累计安装 729 次,近一周安装 31 次,下载 255 次,GitHub 4 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

user-modeling 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
user-modeling

用户建模 · 本页

7294只读MIT有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

User Modeling

Build just enough understanding of your users to make better product decisions.

Why This Exists

Creates behavior-based user models that reveal what users need and how they'll behave, not marketing personas with stock photos.

Input Requirements

This skill works best with:

  • problem-framing output (problem statement, target user, JTBD)
  • Any existing research (interviews, surveys, support tickets, Reddit threads, reviews)

Can also work from assumptions if no research exists—but flags that these need validation.

Workflow

Step 1: Gather Context

Ingest upstream artifacts or ask:

  • Who are you building this for?
  • What do you know about them already?
  • Have you talked to any potential users?
  • Any data sources—reviews, forums, support tickets?

Step 2: Identify User Segments

Look for meaningful differences in:

  • Goals — What are they trying to accomplish?
  • Context — When/where do they encounter the problem?
  • Constraints — What limits their options?
  • Skill level — How sophisticated are they?
  • Frequency — How often do they face this problem?

Not every difference matters. Focus on differences that change *what you'd build*.

Step 3: Build Personas

For each meaningful segment, create a lightweight persona. Limit to 2-3 personas max—more than that dilutes focus.

Step 4: Define Scenarios

For each persona, define 2-3 concrete scenarios where they'd use the product. These become the basis for user stories and flows.

Step 5: Identify Insights

Surface patterns that inform product decisions:

  • What do all personas have in common?
  • Where do they diverge?
  • What would you build differently for each?

Automatically save the output to design/02-user-modeling.md using the Write tool while presenting it to the user.

Output Format

# User Modeling: [Project Name]

## Context
[Brief summary of the problem space and what we know]

**Research basis:**
- [Source 1: what it told us]
- [Source 2: what it told us]
- [Or: "Based on assumptions—needs validation"]

---

## Personas

### Persona 1: [Name/Label]
*[One-line description of who they are]*

**Goals:**
- [Primary goal]
- [Secondary goal]

**Context:**
- [When they encounter the problem]
- [Where they encounter it]
- [What else is going on]

**Pain points:**
- [Frustration 1]
- [Frustration 2]

**Current behavior:**
- [How they solve this today]
- [Tools they use]
- [Workarounds they've developed]

**Constraints:**
- [Time/budget/skill/access limitations]

**What success looks like:**
- [How they'd know the problem is solved]

**Quote:** *"[Something they might say that captures their mindset]"*

---

### Persona 2: [Name/Label]
*[One-line description]*

[Same structure]

---

### Persona 3: [Name/Label]
*[One-line description]*

[Same structure]

---

## Scenarios

### Persona 1 Scenarios

**Scenario 1.1: [Name]**
- **Situation:** [Context—what's happening]
- **Trigger:** [What prompts them to act]
- **Goal:** [What they're trying to accomplish]
- **Current approach:** [How they handle it today]
- **Frustration:** [What's broken about current approach]

**Scenario 1.2: [Name]**
[Same structure]

### Persona 2 Scenarios

**Scenario 2.1: [Name]**
[Same structure]

---

## Key Insights

### Commonalities
[What all personas share—these are table-stakes features]
- [Insight 1]
- [Insight 2]

### Divergences
[Where personas differ—these inform prioritization]
- [Persona 1] needs [X], while [Persona 2] needs [Y]
- [Persona 1] is [context], while [Persona 2] is [different context]

### Design Implications
[How this should influence what you build]
- [Implication 1]
- [Implication 2]
- [Implication 3]

---

## Validation Needed
[What assumptions need testing]
- [ ] [Assumption to validate]
- [ ] [Assumption to validate]

Adaptation Guidelines

Minimal (single obvious user type):

  • One persona, 2-3 scenarios
  • Skip Divergences section
  • 1 page total

Standard (2-3 user types):

  • Full structure as shown
  • 2-3 pages total

Research-heavy (actual user data):

  • Include research summary
  • Add quotes from real users
  • Link to source data in appendix

What Makes a Good Persona

Good persona:

  • Defined by *goals and behaviors*, not demographics
  • Reveals something that changes what you'd build
  • Based on patterns, not individuals
  • Specific enough to make decisions against

Bad persona:

  • Stock photo + age + job title + hobbies
  • So generic it could be anyone
  • Based on one interview or pure assumption
  • Doesn't inform any product decisions

Anti-Patterns to Avoid

  • The Kitchen Sink — Don't add demographics unless they matter
  • The Clone Army — If personas don't differ meaningfully, merge them
  • The Wishful Thinker — Model who users *are*, not who you wish they were
  • The Edge Case Collector — Focus on primary users, not every possible user

Handoff

After presenting the personas, ask:

"Want to move to /solution-scoping to prioritize features, or straight to /prd-generation?"

Note: File is automatically saved to design/02-user-modeling.md for context preservation.

平台分布

Claude Code

30.02%
按下载量换算77

Gemini CLI

19.87%
按下载量换算51

Antigravity

16.19%
按下载量换算41

Codex

11.46%
按下载量换算29

OpenCode

7.74%
按下载量换算20

windsurf

3.38%
按下载量换算9

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。

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