Files
claude-skills-reference/docs/skills/product-team/ux-researcher-designer.md
Alireza Rezvani a68ae3a05e Dev (#305)
* chore: update gitignore for audit reports and playwright cache

* fix: add YAML frontmatter (name + description) to all SKILL.md files

- Added frontmatter to 34 skills that were missing it entirely (0% Tessl score)
- Fixed name field format to kebab-case across all 169 skills
- Resolves #284

* chore: sync codex skills symlinks [automated]

* fix: optimize 14 low-scoring skills via Tessl review (#290)

Tessl optimization: 14 skills improved from ≤69% to 85%+. Closes #285, #286.

* chore: sync codex skills symlinks [automated]

* fix: optimize 18 skills via Tessl review + compliance fix (closes #287) (#291)

Phase 1: 18 skills optimized via Tessl (avg 77% → 95%). Closes #287.

* feat: add scripts and references to 4 prompt-only skills + Tessl optimization (#292)

Phase 2: 3 new scripts + 2 reference files for prompt-only skills. Tessl 45-55% → 94-100%.

* feat: add 6 agents + 5 slash commands for full coverage (v2.7.0) (#293)

Phase 3: 6 new agents (all 9 categories covered) + 5 slash commands.

* fix: Phase 5 verification fixes + docs update (#294)

Phase 5 verification fixes

* chore: sync codex skills symlinks [automated]

* fix: marketplace audit — all 11 plugins validated by Claude Code (#295)

Marketplace audit: all 11 plugins validated + installed + tested in Claude Code

* fix: restore 7 removed plugins + revert playwright-pro name to pw

Reverts two overly aggressive audit changes:
- Restored content-creator, demand-gen, fullstack-engineer, aws-architect,
  product-manager, scrum-master, skill-security-auditor to marketplace
- Reverted playwright-pro plugin.json name back to 'pw' (intentional short name)

* refactor: split 21 over-500-line skills into SKILL.md + references (#296)

* chore: sync codex skills symlinks [automated]

* docs: update all documentation with accurate counts and regenerated skill pages

- Update skill count to 170, Python tools to 213, references to 314 across all docs
- Regenerate all 170 skill doc pages from latest SKILL.md sources
- Update CLAUDE.md with v2.1.1 highlights, accurate architecture tree, and roadmap
- Update README.md badges and overview table
- Update marketplace.json metadata description and version
- Update mkdocs.yml, index.md, getting-started.md with correct numbers

* fix: add root-level SKILL.md and .codex/instructions.md to all domains (#301)

Root cause: CLI tools (ai-agent-skills, agent-skills-cli) look for SKILL.md
at the specified install path. 7 of 9 domain directories were missing this
file, causing "Skill not found" errors for bundle installs like:
  npx ai-agent-skills install alirezarezvani/claude-skills/engineering-team

Fix:
- Add root-level SKILL.md with YAML frontmatter to 7 domains
- Add .codex/instructions.md to 8 domains (for Codex CLI discovery)
- Update INSTALLATION.md with accurate skill counts (53→170)
- Add troubleshooting entry for "Skill not found" error

All 9 domains now have: SKILL.md + .codex/instructions.md + plugin.json

Closes #301

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* feat: add Gemini CLI + OpenClaw support, fix Codex missing 25 skills

Gemini CLI:
- Add GEMINI.md with activation instructions
- Add scripts/gemini-install.sh setup script
- Add scripts/sync-gemini-skills.py (194 skills indexed)
- Add .gemini/skills/ with symlinks for all skills, agents, commands
- Remove phantom medium-content-pro entries from sync script
- Add top-level folder filter to prevent gitignored dirs from leaking

Codex CLI:
- Fix sync-codex-skills.py missing "engineering" domain (25 POWERFUL skills)
- Regenerate .codex/skills-index.json: 124 → 149 skills
- Add 25 new symlinks in .codex/skills/

OpenClaw:
- Add OpenClaw installation section to INSTALLATION.md
- Add ClawHub install + manual install + YAML frontmatter docs

Documentation:
- Update INSTALLATION.md with all 4 platforms + accurate counts
- Update README.md: "three platforms" → "four platforms" + Gemini quick start
- Update CLAUDE.md with Gemini CLI support in v2.1.1 highlights
- Update SKILL-AUTHORING-STANDARD.md + SKILL_PIPELINE.md with Gemini steps
- Add OpenClaw + Gemini to installation locations reference table

Marketplace: all 18 plugins validated — sources exist, SKILL.md present

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* feat(product,pm): world-class product & PM skills audit — 6 scripts, 5 agents, 7 commands, 23 references/assets

Phase 1 — Agent & Command Foundation:
- Rewrite cs-project-manager agent (55→515 lines, 4 workflows, 6 skill integrations)
- Expand cs-product-manager agent (408→684 lines, orchestrates all 8 product skills)
- Add 7 slash commands: /rice, /okr, /persona, /user-story, /sprint-health, /project-health, /retro

Phase 2 — Script Gap Closure (2,779 lines):
- jira-expert: jql_query_builder.py (22 patterns), workflow_validator.py
- confluence-expert: space_structure_generator.py, content_audit_analyzer.py
- atlassian-admin: permission_audit_tool.py
- atlassian-templates: template_scaffolder.py (Confluence XHTML generation)

Phase 3 — Reference & Asset Enrichment:
- 9 product references (competitive-teardown, landing-page-generator, saas-scaffolder)
- 6 PM references (confluence-expert, atlassian-admin, atlassian-templates)
- 7 product assets (templates for PRD, RICE, sprint, stories, OKR, research, design system)
- 1 PM asset (permission_scheme_template.json)

Phase 4 — New Agents:
- cs-agile-product-owner, cs-product-strategist, cs-ux-researcher

Phase 5 — Integration & Polish:
- Related Skills cross-references in 8 SKILL.md files
- Updated product-team/CLAUDE.md (5→8 skills, 6→9 tools, 4 agents, 5 commands)
- Updated project-management/CLAUDE.md (0→12 scripts, 3 commands)
- Regenerated docs site (177 pages), updated homepage and getting-started

Quality audit: 31 files reviewed, 29 PASS, 2 fixed (copy-frameworks.md, governance-framework.md)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix: audit and repair all plugins, agents, and commands

- Fix 12 command files: correct CLI arg syntax, script paths, and usage docs
- Fix 3 agents with broken script/reference paths (cs-content-creator,
  cs-demand-gen-specialist, cs-financial-analyst)
- Add complete YAML frontmatter to 5 agents (cs-growth-strategist,
  cs-engineering-lead, cs-senior-engineer, cs-financial-analyst,
  cs-quality-regulatory)
- Fix cs-ceo-advisor related agent path
- Update marketplace.json metadata counts (224 tools, 341 refs, 14 agents,
  12 commands)

Verified: all 19 scripts pass --help, all 14 agent paths resolve, mkdocs
builds clean.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix: repair 25 Python scripts failing --help across all domains

- Fix Python 3.10+ syntax (float | None → Optional[float]) in 2 scripts
- Add argparse CLI handling to 9 marketing scripts using raw sys.argv
- Fix 10 scripts crashing at module level (wrap in __main__, add argparse)
- Make yaml/prefect/mcp imports conditional with stdlib fallbacks (4 scripts)
- Fix f-string backslash syntax in project_bootstrapper.py
- Fix -h flag conflict in pr_analyzer.py
- Fix tech-debt.md description (score → prioritize)

All 237 scripts now pass python3 --help verification.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix(product-team): close 3 verified gaps in product skills

- Fix competitive-teardown/SKILL.md: replace broken references
  DATA_COLLECTION.md → references/data-collection-guide.md and
  TEMPLATES.md → references/analysis-templates.md (workflow was broken
  at steps 2 and 4)

- Upgrade landing_page_scaffolder.py: add TSX + Tailwind output format
  (--format tsx) matching SKILL.md promise of Next.js/React components.
  4 design styles (dark-saas, clean-minimal, bold-startup, enterprise).
  TSX is now default; HTML preserved via --format html

- Rewrite README.md: fix stale counts (was 5 skills/15+ tools, now
  accurately shows 8 skills/9 tools), remove 7 ghost scripts that
  never existed (sprint_planner.py, velocity_tracker.py, etc.)

- Fix tech-debt.md description (score → prioritize)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* release: v2.1.2 — landing page TSX output, brand voice integration, docs update

- Landing page generator defaults to Next.js TSX + Tailwind CSS (4 design styles)
- Brand voice analyzer integrated into landing page generation workflow
- CHANGELOG, CLAUDE.md, README.md updated for v2.1.2
- All 13 plugin.json + marketplace.json bumped to 2.1.2
- Gemini/Codex skill indexes re-synced
- Backward compatible: --format html preserved, no breaking changes

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

---------

Co-authored-by: alirezarezvani <5697919+alirezarezvani@users.noreply.github.com>
Co-authored-by: Leo <leo@openclaw.ai>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-10 09:48:49 +01:00

12 KiB
Raw Blame History

title, description
title description
UX Researcher & Designer UX Researcher & Designer - Claude Code skill from the Product domain.

UX Researcher & Designer

Domain: Product | Skill: ux-researcher-designer | Source: product-team/ux-researcher-designer/SKILL.md


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
  • 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