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user-modeling用户建模

Agent Skill

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。

总安装

729

周安装

31

GitHub Stars

4

下载量

255
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

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

简介

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。

SKILL.md

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.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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

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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

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来源信息

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