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Agent Skill · 研究检索

ux-researcher-designer(用户体验研究员设计师)

用于辅助界面设计、视觉规范、排版、配色、布局和交互体验优化。它适合让 Agent 根据产品场景整理页面结构、生成 UI 方案、检查视觉一致性或改进组件层级。使用时需要结合现有品牌、设计系统和用户任务,不应只堆装饰元素;涉及真实页面改动时,应通过截图或浏览器预览检查文本溢出、对齐和响应式表现。

ux-researcher-designer 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,权限为需要联网;许可证为 MIT,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 19,996。

总安装 5,376周安装 224GitHub Stars 13,164下载 1,792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:ux-researcher-designer(用户体验研究员设计师)
来源仓库:https://github.com/alirezarezvani/claude-skills
仓库路径:skills/ux-researcher-designer
安装命令:
npx skills add https://github.com/alirezarezvani/claude-skills --skill ux-researcher-designer
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/alirezarezvani/claude-skills --skill ux-researcher-designer

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。

用于用户画像构建、旅程地图绘制和可用性测试方案设计。支持从原始调研数据合成 persona,规划测试流程并输出 actionable 建议。可生成结构化访谈提纲与任务场景,便于团队聚焦用户体验痛点。需用户提供真实用户行为数据或调研素材,避免虚构假设影响结论可靠性。

ux-researcher-designer 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/ux-researcher-designer

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/alirezarezvani/claude-skills --skill ux-resear…;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

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

ux-researcher-designer 怎么安装?

在终端执行 npx skills add https://github.com/alirezarezvani/claude-skills --skill ux-researcher-desig…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

ux-researcher-designer 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。

ux-researcher-designer 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

ux-researcher-designer 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。

ux-researcher-designer 用的人多吗?

公开数据是累计安装 5,376 次,近一周安装 224 次,下载 1,792 次,GitHub 13,164 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。

ux-researcher-designer 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

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。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 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。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

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
ux-researcher-designer

用户体验研究员设计师 · 本页

5,37613,164需要联网MIT通过

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

UX Researcher & Designer

Generate user personas from research data, create journey maps, plan usability tests, and synthesize research findings into actionable design recommendations.


Table of Contents

- Workflow 1: Generate User Persona - Workflow 2: Create Journey Map - Workflow 3: Plan Usability Test - Workflow 4: Synthesize Research


Trigger Terms

Use this skill when you need to:

  • "create user persona"
  • "generate persona from data"
  • "build customer journey map"
  • "map user journey"
  • "plan usability test"
  • "design usability study"
  • "analyze user research"
  • "synthesize interview findings"
  • "identify user pain points"
  • "define user archetypes"
  • "calculate research sample size"
  • "create empathy map"
  • "identify user needs"

Workflows

Workflow 1: Generate User Persona

Situation: You have user data (analytics, surveys, interviews) and need to create a research-backed persona.

Steps:

  1. Prepare user data Required format (JSON): [{"user_id": "user_1", "age": 32, "usage_frequency": "daily", "features_used": ["dashboard", "reports", "export"], "primary_device": "desktop", "usage_context": "work", "tech_proficiency": 7, "pain_points": ["slow loading", "confusing UI"]}]
  2. Run persona generator # Human-readable output python scripts/persona_generator.py # JSON output for integration python scripts/persona_generator.py json
  3. Review generated components Component What to Check Archetype Does it match the data patterns? Demographics Are they derived from actual data? Goals Are they specific and actionable? Frustrations Do they include frequency counts? Design implications Can designers act on these?
  4. Validate persona

- Show to 3-5 real users: "Does this sound like you?" - Cross-check with support tickets - Verify against analytics data

  1. Reference: See references/persona-methodology.md for validity criteria

Workflow 2: Create Journey Map

Situation: You need to visualize the end-to-end user experience for a specific goal.

Steps:

  1. Define scope Element Description Persona Which user type Goal What they're trying to achieve Start Trigger that begins journey End Success criteria Timeframe Hours/days/weeks
  2. Gather journey data Sources:

- User interviews (ask "walk me through...") - Session recordings - Analytics (funnel, drop-offs) - Support tickets

  1. Map the stages Typical B2B SaaS stages: Awareness → Evaluation → Onboarding → Adoption → Advocacy
  2. Fill in layers for each stage Stage: [Name] ├── Actions: What does user do? ├── Touchpoints: Where do they interact? ├── Emotions: How do they feel? (1-5) ├── Pain Points: What frustrates them? └── Opportunities: Where can we improve?
  3. Identify opportunities Priority Score = Frequency × Severity × Solvability
  4. Reference: See references/journey-mapping-guide.md for templates

Workflow 3: Plan Usability Test

Situation: You need to validate a design with real users.

Steps:

  1. Define research questions Transform vague goals into testable questions: Vague Testable "Is it easy to use?" "Can users complete checkout in <3 min?" "Do users like it?" "Will users choose Design A or B?" "Does it make sense?" "Can users find settings without hints?"
  2. Select method Method Participants Duration Best For Moderated remote 5-8 45-60 min Deep insights Unmoderated remote 10-20 15-20 min Quick validation Guerrilla 3-5 5-10 min Rapid feedback
  3. Design tasks Good task format: SCENARIO: "Imagine you're planning a trip to Paris..." GOAL: "Book a hotel for 3 nights in your budget." SUCCESS: "You see the confirmation page." Task progression: Warm-up → Core → Secondary → Edge case → Free exploration
  4. Define success metrics Metric Target Completion rate >80% Time on task <2× expected Error rate <15% Satisfaction >4/5
  5. Prepare moderator guide

- Think-aloud instructions - Non-leading prompts - Post-task questions

  1. Reference: See references/usability-testing-frameworks.md for full guide

Workflow 4: Synthesize Research

Situation: You have raw research data (interviews, surveys, observations) and need actionable insights.

Steps:

  1. Code the data Tag each data point:

- [GOAL] - What they want to achieve - [PAIN] - What frustrates them - [BEHAVIOR] - What they actually do - [CONTEXT] - When/where they use product - [QUOTE] - Direct user words

  1. Cluster similar patterns User A: Uses daily, advanced features, shortcuts User B: Uses daily, complex workflows, automation User C: Uses weekly, basic needs, occasional Cluster 1: A, B (Power Users) Cluster 2: C (Casual User)
  2. Calculate segment sizes Cluster Users % Viability Power Users 18 36% Primary persona Business Users 15 30% Primary persona Casual Users 12 24% Secondary persona
  3. Extract key findings For each theme:

- Finding statement - Supporting evidence (quotes, data) - Frequency (X/Y participants) - Business impact - Recommendation

  1. Prioritize opportunities Factor Score 1-5 Frequency How often does this occur? Severity How much does it hurt? Breadth How many users affected? Solvability Can we fix this?
  2. Reference: See references/persona-methodology.md for analysis framework

Tool Reference

persona_generator.py

Generates data-driven personas from user research data.

ArgumentValuesDefaultDescription
format(none), json(none)Output format

Sample Output:

============================================================
PERSONA: Alex the Power User
============================================================

📝 A daily user who primarily uses the product for work purposes

Archetype: Power User
Quote: "I need tools that can keep up with my workflow"

👤 Demographics:
  • Age Range: 25-34
  • Location Type: Urban
  • Tech Proficiency: Advanced

🎯 Goals & Needs:
  • Complete tasks efficiently
  • Automate workflows
  • Access advanced features

😤 Frustrations:
  • Slow loading times (14/20 users)
  • No keyboard shortcuts
  • Limited API access

💡 Design Implications:
  → Optimize for speed and efficiency
  → Provide keyboard shortcuts and power features
  → Expose API and automation capabilities

📈 Data: Based on 45 users
    Confidence: High

Archetypes Generated:

ArchetypeSignalsDesign Focus
power_userDaily use, 10+ featuresEfficiency, customization
casual_userWeekly use, 3-5 featuresSimplicity, guidance
business_userWork context, team useCollaboration, reporting
mobile_firstMobile primaryTouch, offline, speed

Output Components:

ComponentDescription
demographicsAge range, location, occupation, tech level
psychographicsMotivations, values, attitudes, lifestyle
behaviorsUsage patterns, feature preferences
needs_and_goalsPrimary, secondary, functional, emotional
frustrationsPain points with evidence
scenariosContextual usage stories
design_implicationsActionable recommendations
data_pointsSample size, confidence level

Quick Reference Tables

Research Method Selection

Question TypeBest MethodSample Size
"What do users do?"Analytics, observation100+ events
"Why do they do it?"Interviews8-15 users
"How well can they do it?"Usability test5-8 users
"What do they prefer?"Survey, A/B test50+ users
"What do they feel?"Diary study, interviews10-15 users

Persona Confidence Levels

Sample SizeConfidenceUse Case
5-10 usersLowExploratory
11-30 usersMediumDirectional
31+ usersHighProduction

Usability Issue Severity

SeverityDefinitionAction
4 - CriticalPrevents task completionFix immediately
3 - MajorSignificant difficultyFix before release
2 - MinorCauses hesitationFix when possible
1 - CosmeticNoticed but not problematicLow priority

Interview Question Types

TypeExampleUse For
Context"Walk me through your typical day"Understanding environment
Behavior"Show me how you do X"Observing actual actions
Goals"What are you trying to achieve?"Uncovering motivations
Pain"What's the hardest part?"Identifying frustrations
Reflection"What would you change?"Generating ideas

Knowledge Base

Detailed reference guides in references/:

FileContent
persona-methodology.mdValidity criteria, data collection, analysis framework
journey-mapping-guide.mdMapping process, templates, opportunity identification
example-personas.md3 complete persona examples with data
usability-testing-frameworks.mdTest planning, task design, analysis

Validation Checklist

Persona Quality

  • Based on 20+ users (minimum)
  • At least 2 data sources (quant + qual)
  • Specific, actionable goals
  • Frustrations include frequency counts
  • Design implications are specific
  • Confidence level stated

Journey Map Quality

  • Scope clearly defined (persona, goal, timeframe)
  • Based on real user data, not assumptions
  • All layers filled (actions, touchpoints, emotions)
  • Pain points identified per stage
  • Opportunities prioritized

Usability Test Quality

  • Research questions are testable
  • Tasks are realistic scenarios, not instructions
  • 5+ participants per design
  • Success metrics defined
  • Findings include severity ratings

Research Synthesis Quality

  • Data coded consistently
  • Patterns based on 3+ data points
  • Findings include evidence
  • Recommendations are actionable
  • Priorities justified

Related Skills

  • UI Design System (product-team/ui-design-system/) — Research findings inform design system decisions
  • Product Manager Toolkit (product-team/product-manager-toolkit/) — Customer interview analysis complements persona research

平台分布

Claude Code

26.7%
按下载量换算478

OpenCode

24.5%
按下载量换算439

Gemini CLI

16.65%
按下载量换算298

Codex

12.7%
按下载量换算228

Antigravity

7.61%
按下载量换算136

Cursor

3.54%
按下载量换算63

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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。

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