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name: design-ux-research description: > Use this skill when planning UX research, user research, usability testing, user interviews, personas, user journeys, information architecture, or card sorting. This skill enforces: method selection (generative vs evaluative), structured interview protocols, usability test plans, persona templates, journey mapping, and synthesis frameworks. Do NOT use for: visual design critiques, A/B test design, or analytics/quantitative analysis. version: "1.0.0" author: "j4flmao" license: "MIT" compatibility: claude-code: true cursor: true codex: true windsurf: true tags: [design, research, phase-10]

Design UX Research

Purpose

Design and execute UX research with method selection, structured protocols, and systematic synthesis. Covers generative and evaluative methods, user interview protocols, moderated and unmoderated usability testing, data-driven persona creation, journey mapping, and findings synthesis frameworks. The goal is to reduce design risk by validating assumptions with real user data.

Agent Protocol

Trigger

Exact user phrases: "UX research", "user research", "usability testing", "user interview", "persona", "user journey", "information architecture", "card sorting", "research plan", "research method", "user study", "affinity mapping", "thematic analysis", "moderated test", "unmoderated test", "NPS", "SUS", "CSAT".

Input Context

Before activating, verify:

• Product stage: discovery / alpha / beta / live
• Research question or hypothesis
• Available participants and timeline
• Budget and tools available (remote testing platforms, recording)
• Existing UX artifacts (analytics, support tickets, previous research)
• Stakeholder expectations and key decisions riding on this research
• Acceptable risk level (how wrong can we afford to be?)

Output Artifact

UX research plan with method selection, interview or test protocol, and synthesis framework.

Response Format

• Research plan: method, participants, timeline
• Protocol: questions, tasks, scenarios with time allocations
• Synthesis framework: affinity mapping, persona, journey map
• No preamble. No postamble. No explanations. No filler/hedging/transitions. Compress output — why use many token when few do trick.

Completion Criteria

• Research question defined with hypothesis
• Method selected (generative or evaluative) with rationale
• Participant criteria defined (segments, sample size, screener)
• Interview or test protocol written with timed sections
• Synthesis method chosen (affinity mapping, thematic analysis)
• Output artifacts specified (personas, journey maps, findings report)
• Metrics defined for evaluative research (task completion, SUS, NPS)
• Research artifacts stored in shared repository
• Findings presented to stakeholders with actionable recommendations

Max Response Length

150 lines of plan and protocol.

Framework/Methodology

The Research Funnel

Business Problem
  ↓
Research Goal (what we need to know)
  ↓
Research Question (specific, answerable question)
  ↓
Method Selection (generative vs evaluative)
  ↓
Study Design (participants, protocol, metrics)
  ↓
Data Collection (sessions, surveys, observations)
  ↓
Analysis & Synthesis (patterns, themes, insights)
  ↓
Reporting (findings, recommendations, next steps)

Method Selection by Product Stage

Stage	Question Type	Primary Methods	Secondary Methods	Output
Discovery	What should we build?	Generative interviews, diary studies, field observation	Competitive analysis, contextual inquiry	Problem space definition, user needs
Alpha	Does this concept work?	Concept testing, prototype feedback, co-creation	Card sorting, tree testing	Concept validation, IA direction
Beta	Can users use this?	Moderated usability testing, A/B testing	Surveys, analytics review	Usability issues, metrics baseline
Live	Should we change this?	Unmoderated testing, surveys, analytics	Longitudinal studies, log analysis	Optimization opportunities, satisfaction data

Research Methods Matrix

Method	Type	Phase	Data	Participants	Cost
User interviews	Generative	Discovery	Qualitative	5-8/segment	Medium
Diary study	Generative	Discovery	Qualitative	8-12	High
Field observation	Generative	Discovery	Qualitative	5-10	High
Card sorting	Generative	Alpha	Mixed	20-50	Low
Contextual inquiry	Generative	Discovery	Qualitative	5-8	High
Concept testing	Evaluative	Alpha	Qualitative	5-8	Medium
Moderated usability	Evaluative	Beta	Mixed	5-8	Medium
Unmoderated usability	Evaluative	Beta	Quantitative	20-50	Low
Tree testing	Evaluative	Alpha/Beta	Quantitative	20-50	Low
A/B testing	Evaluative	Live	Quantitative	High volume	Medium
Surveys	Evaluative	Any	Quantitative	50-500	Low
Analytics	Evaluative	Live	Quantitative	All users	Low
Customer feedback	Generative	Live	Qualitative	Variable	Low

Triangulation Framework

Strongest insights come from combining multiple methods:

Qualitative (why?)
  + Quantitative (how many?)
  + Behavioral (what do they actually do?)
  = Triangulated Insight (confident, actionable understanding)

Example: Low NPS score (quantitative) → User interviews (qualitative) reveal top frustration → Session replays (behavioral) confirm frequency → Triangulated insight: specific feature redesign needed.

Workflow

Step 1: Define Research Question & Hypothesis

Format: "How do [users] currently [task] and what prevents them from [desired outcome]?" Hypothesis: "We believe [proposed solution] will [outcome] because [reason]."

Align question with product stage:

Stage	Question Type	Method
Discovery	Open-ended exploration	Interviews, diary studies
Alpha	Concept validation	Concept testing, prototypes
Beta	Usability validation	Moderated/unmoderated testing
Live	Satisfaction/retention	Surveys, analytics

Research question quality checklist:

• Is it specific enough to be answered within the study?
• Is it broad enough to allow unexpected findings?
• Does it focus on user behavior, not product features?
• Does it avoid leading toward a particular answer?
• Would the answer inform a concrete decision?

Step 2: Select Research Method

Method	Type	Participants	Best For
User interviews	Generative	5-8 per segment	Deep understanding, problem discovery
Diary study	Generative	8-12	Longitudinal behavior tracking
Usability testing (moderated)	Evaluative	5-8	In-depth usability issues
Usability testing (unmoderated)	Evaluative	20-50	Scale, task completion metrics
Survey	Evaluative	50-500	Satisfaction, NPS, feature prioritization
Card sorting	Generative	20-50	Information architecture validation
Tree testing	Evaluative	20-50	Navigation findability

Step 3: Participant Recruitment

Define screener criteria: demographics, behavior (frequency of use, tool familiarity), attitudinal (openness to change).

Channel	Cost	Speed	Best For
Existing user base	Low	Fast	Current users
Panels (UserTesting, etc.)	Medium-High	Fast	General population
Social media	Low	Medium	Niche audiences
Intercept/on-site recruitment	Low	Slow	In-product users

Incentives: $50-100/hr for professionals, gift cards for consumers.

Screener best practices:

• 5-10 questions (filtering only, not research questions)
• Include at least one behavior-based question ("How often do you...")
• Include attention check questions to detect bots
• Over-recruit by 20% to account for no-shows
• Confirm consent and recording permission at screening stage

Step 4: Write Research Protocol

Section	Duration	Content
Introduction	5 min	Consent, recording, "there are no wrong answers"
Warm-up	5 min	Background, current tools, role
Main tasks	20-30 min	Scenarios, observe don't guide, think-aloud
Debrief	5 min	Reflection, "anything we missed?"

Question types:

• "Tell me about the last time you [task]" — open-ended, behavioral
• "What was frustrating about that?" — pain points
• "Walk me through how you [subtask]" — process mapping

Avoid: leading questions, hypotheticals ("would you use..."), double-barreled questions.

Probing techniques:

• Active listening: Repeat back what user said to confirm understanding
• Laddering: "Why is that important to you?" repeated until core value emerges
• Silence: Wait 5-7 seconds after user finishes speaking — they often add more
• Clarifying: "Help me understand what you mean by X"
• Echo: Repeat the last 2-3 words as a question

Step 5: Conduct Moderated Usability Testing

Tasks should be scenario-based: "You need to find a specific order from last week. Please try to do that now." Measure: task completion (pass/fail), time-on-task, error rate, satisfaction rating. After each task: Single Ease Question (SEQ) — "Overall, how difficult was this task?" (1-7).

Moderator best practices:

• Read tasks verbatim from protocol (don't paraphrase)
• If user gets stuck, wait 30 seconds before prompting
• Use "What are you thinking?" instead of guiding
• Do NOT help users complete tasks (unless the test measures support needs)
• Take notes on observations, not interpretations
• Switch observers during the session for fresh perspective

Step 6: Synthesize Findings

Method	Output	Tool
Affinity mapping	Themed clusters	Miro, Mural, FigJam, physical sticky notes
Persona creation	Data-driven archetype	Template with goals, frustrations, behaviors
Journey mapping	Visual timeline	Miro, Smaply, LucidChart

Affinity mapping process:

1. Transcribe sessions → extract atomic observations
2. Cluster naturally — no labels yet
3. Label clusters when stable
4. Prioritize by frequency, severity, or business impact
5. Write one insight sentence per cluster

Persona template: Name, role, photo, bio (2-3 sentences), goals (primary + secondary), frustrations (3-5), behaviors, quote.

Persona quality criteria:

• Based on data from at least 3 participants per persona
• Includes specific behaviors, not just demographics
• Distinguishable from other personas (unique goals, pain points)
• Grounded in real quotes and observations
• Actionable for design and product decisions

Journey mapping stages:

1. Define scope: which persona, which scenario, which timeframe
2. List stages: Awareness → Consideration → Decision → Onboarding → Usage → Support
3. Per stage: actions, thoughts, feelings, pain points, opportunities
4. Emotional journey line showing highs and lows
5. Opportunities and recommendations per stage

Step 7: Produce Findings Report

Structure: executive summary → method overview → participant profile → 3-5 key findings (each with evidence, severity) → opportunities/recommendations → appendix.

Every finding links to source evidence (participant quote, task metric, screenshot).

Finding severity classification:

Severity	Definition	Action Required
Critical	Users cannot complete core task	Fix immediately
Major	Users can complete but with significant friction	Fix before next release
Minor	Users complete but experience frustration	Address in near term
Suggestion	Improvement opportunity	Prioritize against other work

Common Pitfalls

Pitfall	Description	Prevention
Leading questions	"How would you improve X?" assumes they should	"Tell me about your current process"
Hypotheticals	"Would you use this feature?" unreliable	Observe behavior with a prototype
Confirmation bias	Asking questions to prove a hypothesis	Listen for disconfirming evidence
Small sample for surveys	<50 responses not statistically significant	Don't report percentages from small samples
Personas based on assumptions	No research behind persona attributes	Every attribute must trace to research data
Ignoring edge cases	Only studying mainstream users	Recruit power users and non-users
No pilot test	Protocol bugs discovered during real sessions	Always pilot with 1-2 internal participants
Solutioneering	Participants become feature request machines	Redirect: "Tell me what you're trying to do"
Culture of agreement	Participants want to be helpful, give positive feedback	Normalize criticism: "We need your honest feedback to improve"
Analysis delay	Waiting too long to synthesize between sessions	Synthesize within 48 hours of each session

Best Practices

Practice	Why
5 participants per segment	Catches ~85% of usability issues (Nielsen Norman)
Pilot test your protocol	Catches ambiguous questions, timing issues
Record sessions	Enables post-hoc analysis and quote extraction
Mix qualitative and quantitative	Qualitative explains why, quantitative confirms patterns
Include a debrief question	"Is there anything we should have asked?" catches blind spots
Take individual notes before group synthesis	Prevents groupthink in analysis
Share raw data alongside synthesized findings	Allows others to verify your conclusions
Timebox synthesis sessions	Prevents analysis paralysis: 2x research time
Present findings as opportunities, not just problems	Drives action, not blame
Research repository enables longitudinal insights	Prevents re-studying known issues

Templates & Tools

Research Plan Template

Project: {project name}
Date: {date}
Researcher: {name}
Stakeholders: {names}

Research Question: {question}
Decision: {what we will decide based on findings}

Method: {method name}
Participants: {N} from {segment}
Session Length: {minutes}
Timeline: {start} to {end}

Protocol Sections:
1. {section} — {duration} — {purpose}
2. {section} — {duration} — {purpose}

Metrics (evaluative only):
- Task completion: target >{X}%
- SEQ: target >{X}
- SUS: target >{X}

Outputs:
- {artifact}
- {artifact}

Usability Test Task Template

Task {N}: {scenario description}
Scenario: "{realistic scenario text}"
Success Criteria: {what counts as task completion}

Measurement:
- [ ] Task completed (first attempt)
- [ ] Task completed (with help)
- [ ] Task failed
- Time on task: {measurement}
- Errors: {count and describe}

Post-task: SEQ score: {1-7 scale}
Post-task: Any comments: {open text}

Observer Notes:
{observations during task}

Research Repository Structure

/research
  /{year}-{project-name}
    /raw-data
      session-recordings/
      transcripts/
      survey-responses.csv
    /analysis
      affinity-map.{tool}
      themes.md
      personas.md
    /reports
      findings-report.{format}
      presentation.{format}
    /artifacts
      consent-form.pdf
      screener.pdf
      protocol.pdf

Tools by Research Activity

Activity	Tools
Session recording	Zoom, Lookback, UserTesting, Riverside
Transcripts	Dovetail, Otter.ai, Rev, Descript
Affinity mapping	Miro, Mural, FigJam, Excel
Survey	Typeform, SurveyMonkey, Google Forms
Card sorting	OptimalSort, UserZoom, Miro
Tree testing	Treejack, UserZoom
Prototype testing	Maze, UserTesting, Lookback
Research repository	Dovetail, Condens, Aurelius, Airtable

Case Studies

Case Study 1: Diary Study Reveals Hidden Workflow

A collaboration SaaS assumed users worked in linear project phases (plan, execute, review). A 2-week diary study with 10 project managers showed that work was actually highly iterative and non-linear with frequent context switching. The study uncovered that the product's linear workflow was the #1 cause of user frustration. Redesigning for non-linear workflows reduced task completion time by 35% and improved satisfaction by 40%.

Method: 14-day diary study with 10 project managers, daily prompts + weekly interviews Key insight: User workflow was non-linear and iterative, opposite of product design assumption Impact: Task completion time -35%, satisfaction +40%

Case Study 2: Unmoderated Usability Testing at Scale

An e-commerce platform needed to test a redesigned checkout flow. Using an unmoderated testing platform, they collected data from 45 participants in 3 days (vs 2-3 weeks for moderated). The study identified 7 critical issues and 12 minor issues. The critical issues included: unclear shipping cost display, confusing payment options, and a non-obvious "apply coupon" interaction. Fixing these issues recovered an estimated 8% in checkout conversion.

Method: Unmoderated usability testing (45 participants, 3 days) Key insight: Cost-effective scale unmoderated testing catches issues quickly Impact: 8% checkout conversion recovered by fixing critical issues

Case Study 3: Persona-Driven Redesign

A B2B analytics platform had a single dashboard design for all users. UX research involving 18 interviews and a 200-person survey revealed 4 distinct user types with fundamentally different needs. The "Executive" persona needed high-level KPIs and trends; the "Analyst" persona needed raw data exploration and export. Creating persona-specific dashboards increased daily active usage by 45% and reduced support tickets by 30%.

Method: 18 interviews + 200-response survey → 4 validated personas Key insight: One-size-fits-all design was serving no segment well Impact: DAU +45%, support tickets -30%

Rules

• Interview questions are always open-ended first — never lead the participant
• Usability tests measure task completion, time-on-task, error rate, and satisfaction
• Personas are based on research data, not assumptions
• Journey maps include emotional highs and lows — not just actions
• Findings without evidence are opinions — tag every finding to a participant quote
• Sample size of 5 per segment catches ~85% of usability issues (Nielsen Norman)
• Never use customers as designers — ask "what do you do", not "what should we build"
• Always pilot test the protocol with 1-2 internal participants before real sessions
• Record sessions only with explicit participant consent
• Transcribe sessions within 72 hours for accurate analysis
• Decompress between sessions (take 10 min minimum break)
• Include observers from cross-functional teams in at least some sessions
• Report severity alongside every finding
• Limit survey length to 20 questions or 10 minutes
• Debrief as a team within 2 hours of each session day
• Timebox affinity mapping to avoid analysis paralysis (2x session time)
• Offer participants the option to review their quotes in context before publication

References

• references/research-methods.md — Research Methods Reference
• references/synthesis-frameworks.md — Synthesis Frameworks Reference
• references/synthesis.md — Synthesis
• references/usability-testing-guide.md — Usability Testing Guide
• references/ux-research-advanced.md — Ux Research Advanced Topics
• references/ux-research-fundamentals.md — Ux Research Fundamentals
• references/ux-research-methods.md — UX Research Methods
• references/ux-research-data-analysis.md — UX Research Data Analysis

Handoff

design-prototyping for interactive prototypes based on research insights. Carry forward: research findings, persona profiles, journey maps.

No preamble. No postamble. No explanations. No filler/hedging/transitions. Compress output — why use many token when few do trick.

Implementation Patterns

Observer Pattern for Event Handling

` interface EventObserver { onEvent(event: T): Promise; }

class EventBus { private observers: Set<EventObserver> = new Set(); subscribe(observer: EventObserver): void { this.observers.add(observer); } unsubscribe(observer: EventObserver): void { this.observers.delete(observer); } async emit(event: T): Promise { const results = Array.from(this.observers).map(o => o.onEvent(event)); await Promise.allSettled(results); } } `

Configuration-Driven Approach

config: defaults: timeout: 30s retryCount: 3 overrides: production: timeout: 60s retryCount: 5 development: timeout: 300s retryCount: 1

Production Considerations

Deployment Checklist

• Configuration validated against schema before startup
• Health check endpoints registered and monitored
• Graceful shutdown with draining period (30s timeout)
• Resource limits configured (CPU, memory, file descriptors)
• Log level set appropriate for environment
• Metrics endpoint secured and exposed
• Rate limiting configured per-tier
• TLS certificates valid and auto-renewing
• Database migrations run as separate deployment step
• Feature flags ready for gradual rollout

Monitoring and Alerting

Metric	Threshold	Severity	Action
Error rate	> 1% over 5min	Critical	Page on-call
p99 latency	> 2s over 5min	Warning	Investigate
Throughput drop	> 50% over 1min	Critical	Check upstream
Queue depth	> 1000 over 1min	Warning	Scale consumers
Disk usage	> 85%	Warning	Clean or expand
Memory usage	> 90% heap	Critical	Restart or scale

Anti-Patterns

Anti-Pattern	Symptom	Root Cause	Solution
Premature optimization	Complex code for no measured benefit	Guessing instead of profiling	Measure first, optimize based on data
Copy-paste reuse	Duplicate code across codebase	Lack of abstraction	Extract shared logic into libraries
Gold-plating	Features with no current requirement	Over-engineering	YAGNI — build what's needed now
Magical thinking	Assumptions without validation	Skipping error handling	Handle all failure modes explicitly

Performance Optimization

Caching Strategy

Cache hierarchy: L1 (in-memory local) → L2 (distributed Redis/Memcached) → L3 (CDN/Edge). Cache invalidation: TTL-based (simple, stale), event-based (complex, fresh), write-through (consistent, higher write latency), write-behind (fast writes, eventual consistency).

Resource Pooling

• Database connections: Pool of reusable connections (HikariCP, pgBouncer)
• HTTP connections: Keep-alive + connection pooling for external calls
• Thread pool: Bounded thread pools for async task execution

Profiling Methodology

1. Establish baseline with production traffic profile
2. Profile CPU with sampling profiler (pprof, perf, async-profiler)
3. Profile memory with heap dumps and allocation tracking
4. Profile I/O with strace/perf trace for syscall analysis
5. Profile latency with distributed tracing (OpenTelemetry)
6. Identify bottleneck, formulate hypothesis, implement fix
7. Re-profile to verify improvement, repeat

Security Considerations

Threat Modeling (STRIDE)

• Spoofing: Identity validation, authentication
• Tampering: Integrity checks, digital signatures
• Repudiation: Audit logs, non-repudiation
• Information disclosure: Encryption, access control
• Denial of service: Rate limiting, resource quotas
• Elevation of privilege: Principle of least privilege

Supply Chain Security

• Dependency scanning: Snyk, Dependabot, Trivy
• SBOM generation: CycloneDX or SPDX format
• Signed commits: GPG or SSH commit signing
• Artifact verification: Checksum validation, signature verification

Secrets Management

• Secrets never in code — always in secrets manager (Vault, AWS Secrets Manager)
• Rotation policy: Rotate database credentials every 90 days
• Access audit: Log every secrets access, alert on anomalies
• Encryption at rest and in transit for all secrets
• Principle of least privilege: each service gets only its own secrets

Architecture Decision Trees

Research Method Decision Tree

What phase is the product in? ├── Discovery → Generative research (interviews, diary studies, field observation) ├── Definition → Descriptive research (surveys, competitive analysis, analytics) └── Evaluation → Evaluative research (usability testing, A/B testing, tree testing) What is the research question type? ├── Behavioral (what users do) → Analytics, usability testing, field studies └── Attitudinal (what users say) → Surveys, interviews, focus groups Required confidence level? ├── High (statistically significant) → Quantitative (surveys, A/B tests, analytics) └── Medium (directional) → Qualitative (interviews, usability tests, diary studies)

Participant Recruiting Decision Tree

Who are the target users? ├── Existing customers → In-app recruitment, CRM outreach, email lists └── New/prospective users → Third-party panels (UserInterviews, Respondent), social media How many participants needed? ├── Qualitative (5-8 per segment) → Saturated insights, detailed feedback └── Quantitative (100+ per segment) → Statistical significance, segmentation analysis Budget for incentives? ├── Yes → Professional recruiting with screened participants └── No → Internal recruiting, friends-and-family, social media

Test Plan: [Feature Name]

Goals

• [Primary research question]
• [Secondary research question]

Methodology

• Type: Moderated/Unmoderated remote
• Duration: 45 minutes
• Participants: 6-8 per segment

Tasks

1. [Task name] - [Success criteria] - [Metric: success rate, time on task]
2. [Task name] - [Success criteria] - [Metric: success rate, time on task]

Analysis Plan

• Task success rate threshold: 80%
• Time on task benchmark: < 2 minutes
• SUS score target: > 68

Artifacts

• Task scenarios document
• Consent form
• Screen reader checklist `