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vasilyu1983

software-ux-research

by vasilyu1983

25🍴 6📅 Jan 23, 2026

SKILL.md


name: software-ux-research description: UX research and analysis for discovering, validating, and evaluating software experiences, including research ops, governance, and measurement.

Software UX Research Skill — Quick Reference

Use this skill to identify problems/opportunities and de-risk decisions. Use software-ui-ux-design to implement UI patterns, component changes, and design system updates.


Dec 2025 Baselines (Core)

When to Use This Skill

  • Discovery: user needs, JTBD, opportunity sizing, mental models.
  • Validation: concepts, prototypes, onboarding/first-run success.
  • Evaluative: usability tests, heuristic evaluation, cognitive walkthroughs.
  • Quant/behavioral: funnels, cohorts, instrumentation gaps, guardrails.
  • Research Ops: intake, prioritization, repository/taxonomy, consent/PII handling.
  • Demographic research: Age-diverse, cultural, accessibility participant recruitment.
  • A/B testing: Experiment design, sample size, analysis, pitfalls.

When NOT to Use This Skill

  • UI implementation → Use software-ui-ux-design for components, patterns, code
  • Analytics instrumentation → Use software-observability for event tracking setup
  • Accessibility compliance audit → Use accessibility-specific checklists (WCAG conformance)
  • Marketing research → Use marketing-* skills for market sizing, customer acquisition
  • A/B test platform setup → Use experimentation platforms (Statsig, GrowthBook, LaunchDarkly)

Operating Mode (Core)

If inputs are missing, ask for:

  • Decision to unblock (what will change based on this research).
  • Target roles/segments and top tasks.
  • Platforms and contexts (web/mobile/desktop; remote/on-site; assisted tech).
  • Existing evidence (analytics, tickets, reviews, recordings, prior studies).
  • Constraints (timeline, recruitment access, compliance, budget).

Default outputs (pick what the user asked for):


Method Chooser (Core)

Research Types (Keep Explicit)

TypeGoalPrimary Outputs
DiscoveryUnderstand needs and contextJTBD, opportunity areas, constraints
ValidationReduce solution riskGo/no-go, prioritization signals
EvaluativeImprove usability/accessibilitySeverity-rated issues + fixes

Decision Tree (Fast)

What do you need?
  ├─ WHY / needs / context → interviews, contextual inquiry, diary
  ├─ HOW / usability → moderated usability test, cognitive walkthrough, heuristic eval
  ├─ WHAT / scale → analytics/logs + targeted qual follow-ups
  └─ WHICH / causal → experiments (if feasible) or preference tests

Method Selection Table (Practical)

QuestionBest methodsAvoid whenOutput
What problems matter most?Interviews, contextual inquiry, diaryOnly surveys/analyticsProblem framing + evidence
Can users complete key tasks?Moderated usability tests, task analysisStakeholder reviewTask success + issue list
Is navigation findable?Tree test, first-click, card sortExtremely small audience [Inference]IA changes + labels
What is happening at scale?Funnels, cohorts, logs, support taxonomyInstrumentation missingBaselines + segments + drop-offs
Which variant performs better?A/B, switchback, holdoutInsufficient power or high riskDecision with confidence + guardrails

Research by Product Stage

Stage Framework (What to Do When)

StageDecisionsPrimary MethodsSecondary MethodsOutput
DiscoveryWhat to build and for whomInterviews, field/diary, journey mappingCompetitive analysis, feedback miningOpportunity brief + JTBD
Concept/MVPDoes the concept work?Concept test, prototype usabilityFirst-click/tree testMVP scope + onboarding plan
LaunchIs it usable + accessible?Usability testing, accessibility reviewHeuristic eval, session replayLaunch blockers + fixes
GrowthWhat drives adoption/value?Segmented analytics + qual follow-upsChurn interviews, surveysRetention drivers + friction
MaturityWhat to optimize/deprecate?Experiments, longitudinal trackingUnmoderated testsIncremental roadmap

Post-Launch Measurement (What to Track)

Metric categoryWhat it answersPair with
AdoptionAre people using it?Outcome/value metric
ValueDoes it help users succeed?Adoption + qualitative reasons
ReliabilityDoes it fail in ways users notice?Error rate + recovery success
AccessibilityCan diverse users complete flows?Assistive-tech coverage + defect trends

Research for Complex Systems (Workflows, Admin, Regulated)

Complexity Indicators

IndicatorExampleResearch Implication
Multi-step workflowsDraft → approve → publishTask analysis + state mapping
Multi-role permissionsAdmin vs editor vs viewerTest each role + transitions
Data dependenciesRequires integrations/syncError-path + recovery testing
High stakesFinance, healthcareSafety checks + confirmations
Expert usersDev tools, analyticsRecruit real experts (not proxies)

Evaluation Methods (Core)

  • Contextual inquiry: observe real work and constraints.
  • Task analysis: map goals → steps → failure points.
  • Cognitive walkthrough: evaluate learnability and signifiers.
  • Error-path testing: timeouts, offline, partial data, permission loss, retries.
  • Multi-role walkthrough: simulate handoffs (creator → reviewer → admin).

Multi-Role Coverage Checklist

  • Role-permission matrix documented.
  • “No access” UX defined (request path, least-privilege defaults).
  • Cross-role handoffs tested (notifications, state changes, audit history).
  • Error recovery tested for each role (retry, undo, escalation).

Research Ops & Governance (Core)

Intake (Make Requests Comparable)

Minimum required fields:

  • Decision to unblock and deadline.
  • Research questions (primary + secondary).
  • Target users/segments and recruitment constraints.
  • Existing evidence and links.
  • Deliverable format + audience.

Prioritization (Simple Scoring)

Use a lightweight score to avoid backlog paralysis:

  • Decision impact
  • Knowledge gap
  • Timing urgency
  • Feasibility (recruitment + time)

Repository & Taxonomy

  • Store each study with: method, date, product area, roles, tasks, key findings, raw evidence links.
  • Tag for reuse: problem type (navigation/forms/performance), component/pattern, funnel step.
  • Prefer “atomic” findings (one insight per card) to enable recombination [Inference].

Follow applicable privacy laws; GDPR is a primary reference for EU processing https://eur-lex.europa.eu/eli/reg/2016/679/oj

PII handling checklist:

  • Collect minimum PII needed for scheduling and incentives.
  • Store identity/contact separately from study data.
  • Redact names/emails from transcripts before broad sharing.
  • Restrict raw recordings to need-to-know access.
  • Document consent, purpose, retention, and opt-out path.

Measurement & Decision Quality (Core)

Research ROI Quick Reference

Research ActivityProxy MetricCalculation
Usability testing findingPrevented dev reworkHours saved × $150/hr
Discovery interviewPrevented build-wrong-thingSprint cost × risk reduction %
A/B test conclusive resultImproved conversion(ΔConversion × Traffic × LTV) - Test cost
Heuristic evaluationEarly defect detectionDefects found × Cost-to-fix-later

Rules of thumb:

  • 1 usability finding that prevents 40 hours of rework = $6,000 value
  • 1 discovery insight that prevents 1 wasted sprint = $50,000-100,000 value
  • Research that improves conversion 0.5% on 100k visitors × $50 LTV = $25,000/month

Triangulation Rubric

ConfidenceEvidence requirementUse for
HighMultiple methods or sources agreeHigh-impact decisions
MediumStrong signal from one method + supporting indicatorsPrioritization
LowSingle source / small sampleExploratory hypotheses

Adoption vs Value (Avoid Vanity Metrics)

Metric typeExampleCommon pitfall
AdoptionFeature usage rate“Used” ≠ “helpful”
Value/outcomeTask success, goal completionHarder to instrument

When NOT to Run A/B Tests

SituationWhy it failsBetter method
Low power/trafficInconclusive resultsUsability tests + trends
Many variables changeAttribution impossiblePrototype tests → staged rollout
Need “why”Experiments don’t explainInterviews + observation
Ethical constraintsHarmful denialPhased rollout + holdouts
Long-term effectsShort tests miss delayed impactLongitudinal + retention analysis

Common Confounds (Call Out Early)

  • Selection bias (only power users respond).
  • Survivorship bias (you miss churned users).
  • Novelty effect (short-term lift).
  • Instrumentation changes mid-test (metrics drift).

Optional: AI/Automation Research Considerations

Use only when researching automation/AI-powered features. Skip for traditional software UX.

2026 benchmark: 88% of UX researchers identify AI-assisted analysis as the top trend (UXStudioTeam survey). Use AI for efficiency while maintaining human judgment on strategy and interpretation.

Key Questions

DimensionQuestionMethods
Mental modelWhat do users think the system can/can’t do?Interviews, concept tests
Trust calibrationWhen do users over/under-rely?Scenario tests, log review
Explanation usefulnessDoes “why” help decisions?A/B explanation variants, interviews
Failure recoveryDo users recover and finish tasks?Failure-path usability tests

Error Taxonomy (User-Visible)

Failure typeTypical impactWhat to measure
Wrong outputRework, lost trustVerification + override rate
Missing outputManual fallbackFallback completion rate
Unclear outputConfusionClarification requests
Non-recoverable failureBlocked flowTime-to-recovery, support contact

Optional: AI-Assisted Research Ops (Guardrailed)

  • Use automation for transcription/tagging only after PII redaction.
  • Maintain an audit trail: every theme links back to raw quotes/clips.

Synthetic Users: When Appropriate (2026)

48% of researchers see synthetic/AI participants as impactful. Use with clear boundaries:

Use CaseAppropriate?Why
Early concept brainstorming⚠️ Supplement onlyGenerate edge cases, not validation
Scenario/edge case expansion✅ YesBroaden coverage before real testing
Moderator training/practice✅ YesPractice without participant burden
Hypothesis generation✅ YesExplore directions to test with real users
Validation/go-no-go decisions❌ NeverCannot substitute lived experience
Usability findings as evidence❌ NeverReal behavior required
Quotes in reports❌ NeverFabricated quotes damage credibility

Critical rule: Synthetic outputs are hypotheses, not evidence. Always validate with real users before shipping.


Resources

Core Research Methods:

Demographic & Quantitative Research (NEW):

Data & Sources:


Trend Awareness Protocol

IMPORTANT: When users ask recommendation questions about UX research, you MUST use WebSearch to check current trends before answering.

Trigger Conditions

  • "What's the best UX research tool for [use case]?"
  • "What should I use for [usability testing/surveys/analytics]?"
  • "What's the latest in UX research?"
  • "Current best practices for [user interviews/A/B testing/accessibility]?"
  • "Is [research method] still relevant in 2026?"
  • "What research tools should I use?"
  • "Best approach for [remote research/unmoderated testing]?"

Required Searches

  1. Search: "UX research trends 2026"
  2. Search: "UX research tools best practices 2026"
  3. Search: "[Maze/Hotjar/UserTesting] comparison 2026"
  4. Search: "AI in UX research 2026"

What to Report

After searching, provide:

  • Current landscape: What research methods/tools are popular NOW
  • Emerging trends: New techniques or tools gaining traction
  • Deprecated/declining: Methods that are losing effectiveness
  • Recommendation: Based on fresh data and current practices
  • AI-powered research tools (Maze AI, Looppanel)
  • Unmoderated testing platforms evolution
  • Voice of Customer (VoC) platforms
  • Analytics and behavioral tools (Hotjar, FullStory)
  • Accessibility testing tools and standards
  • Research repository and insight management

Templates

Score

Total Score

60/100

Based on repository quality metrics

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