AGENT SKILL

ux-researcher-designer

UX research and design toolkit for Senior UX Designer/Researcher including data-driven persona generation, journey mapping, usability testing frameworks, and research synthesis. Use for user research, persona creation, journey mapping, and design validation.

License 许可
MIT
最近更新
2026-09-22
Stars
★ 0
Forks
0

01 前言 Front Matter

name
ux-researcher-designer
description
UX research and design toolkit for Senior UX Designer/Researcher including data-driven persona generation, journey mapping, usability testing frameworks, and research synthesis. Use for user research, persona creation, journey mapping, and design validation.

02 资源文件 6

03 SKILL.md 正文

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


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
  5. 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
  3. Map the stages

    Typical B2B SaaS stages:

    Awareness → Evaluation → Onboarding → Adoption → Advocacy
    
  4. 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?
    
  5. Identify opportunities

    Priority Score = Frequency × Severity × Solvability

  6. 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
  6. 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
  2. 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)
    
  3. Calculate segment sizes

    Cluster Users % Viability
    Power Users 18 36% Primary persona
    Business Users 15 30% Primary persona
    Casual Users 12 24% Secondary persona
  4. Extract key findings

    For each theme:

    • Finding statement
    • Supporting evidence (quotes, data)
    • Frequency (X/Y participants)
    • Business impact
    • Recommendation
  5. 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?
  6. Reference: See references/persona-methodology.md for analysis framework


Tool Reference

persona_generator.py

Generates data-driven personas from user research data.

Argument Values Default Description
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:

Archetype Signals Design Focus
power_user Daily use, 10+ features Efficiency, customization
casual_user Weekly use, 3-5 features Simplicity, guidance
business_user Work context, team use Collaboration, reporting
mobile_first Mobile primary Touch, offline, speed

Output Components:

Component Description
demographics Age range, location, occupation, tech level
psychographics Motivations, values, attitudes, lifestyle
behaviors Usage patterns, feature preferences
needs_and_goals Primary, secondary, functional, emotional
frustrations Pain points with evidence
scenarios Contextual usage stories
design_implications Actionable recommendations
data_points Sample size, confidence level

Quick Reference Tables

Research Method Selection

Question Type Best Method Sample Size
"What do users do?" Analytics, observation 100+ events
"Why do they do it?" Interviews 8-15 users
"How well can they do it?" Usability test 5-8 users
"What do they prefer?" Survey, A/B test 50+ users
"What do they feel?" Diary study, interviews 10-15 users

Persona Confidence Levels

Sample Size Confidence Use Case
5-10 users Low Exploratory
11-30 users Medium Directional
31+ users High Production

Usability Issue Severity

Severity Definition Action
4 - Critical Prevents task completion Fix immediately
3 - Major Significant difficulty Fix before release
2 - Minor Causes hesitation Fix when possible
1 - Cosmetic Noticed but not problematic Low priority

Interview Question Types

Type Example Use 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/:

File Content
persona-methodology.md Validity criteria, data collection, analysis framework
journey-mapping-guide.md Mapping process, templates, opportunity identification
example-personas.md 3 complete persona examples with data
usability-testing-frameworks.md Test 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

04 同仓库更多 Skills

Mabbit143
Mabbit143/Claude-SKills

syllabus

Generates a curated supplementary reading list from any course syllabus using Consensus academic search. Grill-me intake (syllabus input format + course audience + year range) plus a grouping forcing-options checkpoint before any search runs — so the reading list matches the course's level and recency need. Parses the syllabus to extract topics and learning outcomes, searches Consensus for recent peer-reviewed papers per topic, and produces a professionally formatted .docx with clickable Consensus links, plain-language summaries calibrated to audience level, and Bloom-higher-order discussion questions tied to course learning goals. Triggers whenever a user uploads a syllabus, course outline, or curriculum document and wants supplementary readings. Also triggers on: 'syllabus reading list', 'find papers for my course', 'create a reading list from this syllabus', 'recent research for my class', 'supplementary readings', 'find journal articles for these topics', 'what recent papers cover this material', 'any new research on these course topics', 'update my syllabus with recent papers'. Even casual mentions when a syllabus is attached should trigger this skill.

★ 0
Mabbit143
Mabbit143/Claude-SKills

research

Default entry point for any research request — a hybrid router that classifies the question deterministically and either delegates to a specialist research skill (pulse for trends/sentiment, grants for NIH funding, litreview for academic literature, syllabus for course reading, patent for prior-art + IP landscape, dossier for entity research) or runs its own plan-decompose-multi-source-search-synthesize-cite fallback workflow when no specialist matches. Always surfaces the routing decision so users can override. Triggers — "research [topic]", "look into [topic]", "what do we know about [topic]", "investigate [topic]", "find me information on [topic]", "do some research on [topic]", "I need to understand [topic]", or any research request that doesn't obviously match a more-specific specialist skill. Output is a markdown briefing (default) or .docx document (on request) with full citations and an audit log.

★ 0
Mabbit143
Mabbit143/Claude-SKills

pulse

Multi-source recency research skill that takes the pulse of any topic across Reddit, Hacker News, the open web, and optionally X/Twitter within a configurable recent window (default 30 days). Forcing intake clarifies topic specificity, angle (trend/sentiment/problems/opportunities/comparison), time window, and platform scope before searching. Returns a synthesized briefing with citations, engagement metrics, and cross-platform pattern analysis. Triggers: 'pulse on [topic]', 'what's happening with [topic]', 'what are people saying about [topic]', 'current conversation about [topic]', 'take the pulse of [topic]', 'trending: [topic]', 'find me info on [topic]', or any variation requesting multi-source recency intelligence on a topic. Also use for competitor research, trend discovery, tool comparisons, and audience sentiment analysis.

★ 0
Mabbit143
Mabbit143/Claude-SKills

patent

Patent prior-art and landscape intelligence skill — not generic patent help. Commits to one of five sub-use-cases via forcing intake (novelty search / freedom-to-operate / competitive landscape / acquisition diligence / litigation prior-art) before any search runs. Searches Google Patents, Espacenet, USPTO, and optionally Lens.org for citation-graph signals. Output is an editable Word document (.docx) with verdict, ranked closest art (claim-text extracted), CPC-class-aware landscape, family-resolved hits, geographic coverage, FTO flags where applicable, strategy recommendations, and full audit log. Triggers: 'prior art search for [invention]', 'patent search on [topic]', 'freedom to operate analysis', 'FTO for [product]', 'patent landscape for [field]', 'is [invention] novel', 'patents on [topic]', 'competitive patent analysis', 'prior art for litigation', 'patent diligence on [company]'. Produces search signal, not legal advice — always recommends consulting a patent attorney before filing or licensing decisions. Trademark, copyright, and trade-secret questions are out of scope.

★ 0
Mabbit143
Mabbit143/Claude-SKills

notebooklm

Browser automation skill for controlling Google's NotebookLM. Handles reading and querying notebooks, adding sources (URLs, text, files, YouTube links, synthesized content), generating Studio outputs (Audio Overview, infographics, slide decks, study guides, briefing docs, mind maps, timelines, FAQs), and creating new notebooks. Triggers on any phrase involving NotebookLM — 'open NotebookLM', 'check my [name] notebook', 'pull info from NotebookLM', 'ask my notebook about X', 'add [source] to NotebookLM', 'create an infographic in NotebookLM', 'use NotebookLM Studio', 'generate a slide deck from my notebook', or any variation where the goal involves NotebookLM. Requires browser automation environment — fails gracefully when unavailable.

★ 0
Mabbit143
Mabbit143/Claude-SKills

litreview

Academic literature orientation skill that searches papers via Consensus, builds a strategic search plan using PICO (default) or SPIDER / Decomposition / hybrid as fallbacks, and synthesizes findings into a professionally formatted Word document (.docx) research guide. Grill-me intake (research question specificity + framework hint + tentative depth) before the recon search; a second forcing checkpoint after Phase 2 confirms framework + sub-areas + depth before searches consume budget. Configurable depth (5/10/20 queries) controls coverage vs. speed. Output is a 'launching pad' — not a finished review, but an orientation guide that lets a researcher dive in confidently. Triggers: 'litreview on [topic]', 'literature review on [topic]', 'I'm starting a literature review on X', 'I'm writing a paper on X', 'help me research X', 'I'm doing research on X', 'can you help me research X'. Do NOT trigger for single one-off paper searches where the user just wants a quick list — that's a plain Consensus search.

★ 0