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.

Mar 2026 Baselines (Core)

• Human-centred design: Iterative design + evaluation grounded in evidence (ISO 9241-210:2019) https://www.iso.org/standard/77520.html

• Usability definition: Effectiveness, efficiency, satisfaction in context (ISO 9241-11:2018) https://www.iso.org/standard/63500.html

• Accessibility baseline: WCAG 2.2 is a W3C Recommendation (12 Dec 2024) https://www.w3.org/TR/WCAG22/

• WCAG 3.0 preview: Working Draft published Sep 2025; introduces Bronze/Silver/Gold conformance tiers and enhanced cognitive accessibility; not expected before 2028-2030 https://www.w3.org/WAI/standards-guidelines/wcag/wcag3-intro/

• EU shipping note: European Accessibility Act applies to covered products/services after 28 Jun 2025 (Directive (EU) 2019/882) https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32019L0882

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.

• Non-technical user research: Digital literacy assessment, simplified-flow validation, low-tech-confidence usability testing.

When NOT to Use This Skill

• UI implementation → Use software-ui-ux-design for components, patterns, code

• Analytics instrumentation → Use marketing-product-analytics for tracking plans and qa-observability for implementation patterns

• Accessibility compliance audit → Use accessibility-specific checklists (WCAG conformance)

• Marketing research → Use marketing-social-media or related marketing skills

• 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):

• Research plan + output contract (prefer ../software-clean-code-standard/assets/checklists/ux-research-plan-template.md; use assets/research-plan-template.md for skill-specific detail)

• Study protocol (tasks/script + success metrics + recruitment plan)

• Findings report (issues + severity + evidence + recommendations + confidence)

• Decision brief (options + tradeoffs + recommendation + measurement plan)

Required Output Sections

Every research output — plans, protocols, evaluations, reports — must include these sections. They represent the skill's core value beyond standard UX knowledge: governance, confidence calibration, and ethical research practice.

Method Justification: Name the chosen method AND explain why alternatives were rejected. Do not just describe the method; explain why it was selected over at least 2 alternatives given the specific context (stage, timeline, sample, question type).

Confidence & Triangulation Assessment: Tag every recommendation or finding with a confidence level:

Confidence Evidence requirement Use for

High Multiple methods or sources agree High-impact decisions

Medium Strong signal from one method + supporting indicators Prioritization

Low Single source / small sample Exploratory hypotheses only

Consent & Data Handling: Include a PII/consent section in every plan or protocol. Research that involves participants requires explicit attention to:

• Minimum PII collection

• Identity stored separately from study data

• Name/email redaction before broad sharing

• Recording access restricted to need-to-know

• Consent, purpose, retention, and opt-out documented

Decision Framework: For evaluations and analysis outputs, provide a structured decision table with options, confidence levels, timelines, and risks — not just a single recommendation.

Pre-Decision Checklist: For experiment evaluations (A/B tests, etc.), include a verification checklist of confounds and data quality checks to complete before any ship/kill decision.

Method Chooser (Core)

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

When selecting a method, always justify the choice by explaining why 2+ alternatives were rejected given the user's specific context. This is a key differentiator — generic "we'll do interviews" without justification is insufficient.

Research by Product Stage

Stage Framework (What to Do When)

Stage Decisions Primary Methods Secondary Methods Output

Discovery What to build and for whom Interviews, field/diary, journey mapping Competitive analysis, feedback mining Opportunity brief + JTBD + Forces of Progress

Concept/MVP Does the concept work? Concept test, prototype usability First-click/tree test MVP scope + onboarding plan

Launch Is it usable + accessible? Usability testing, accessibility review Heuristic eval, session replay Launch blockers + fixes

Growth What drives adoption/value? Segmented analytics + qual follow-ups Churn interviews, surveys Retention drivers + friction

Maturity What to optimize/deprecate? Experiments, longitudinal tracking Unmoderated tests Incremental roadmap

Discovery Outputs: Beyond Basic JTBD

Discovery research should produce more than job statements. Include:

• Forces of Progress diagram: Map the four forces acting on switching behavior — Push (current pain), Pull (new solution appeal), Anxiety (fear of change), Habit (inertia). These forces explain why users do or don't adopt, which directly informs positioning and onboarding.

• Pain Point Severity Matrix: Score each pain point by Frequency × Impact × Breadth to prioritize objectively. A pain that affects 3 roles weekly outranks one that affects 1 role monthly, even if the single-role pain feels more dramatic in interviews.

Research for Complex Systems (Workflows, Admin, Regulated)

Complexity Indicators

Indicator Example Research Implication

Multi-step workflows Draft → approve → publish Task analysis + state mapping

Multi-role permissions Admin vs editor vs viewer Test each role + transitions

Data dependencies Requires integrations/sync Error-path + recovery testing

High stakes Finance, healthcare Safety checks + confirmations

Expert users Dev tools, analytics Recruit 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].

Consent, PII, and Access Control

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.

Research Democratization (2026 Trend)

Research democratization is a recurring 2026 trend: non-researchers increasingly conduct research. Enable carefully with guardrails.

Approach Guardrails Risk Level

Templated usability tests Script + task templates provided Low

Customer interviews by PMs Training + review required Medium

Survey design by anyone Central review + standard questions Medium

Unsupervised research Not recommended High

Guardrails for non-researchers:

• Pre-approved research templates only

• Central review of findings before action

• No direct participant recruitment without ops approval

• Mandatory bias awareness training

• Clear escalation path for unexpected findings

Researching Non-Technical User Segments (2026)

Quick checklist for research involving users with low digital literacy or low tech confidence. Full guidance in references/non-technical-user-research.md.

• Assess digital literacy tier (excluded → dependent → hesitant → capable → confident)

• Recruit via offline-first channels (community centers, libraries, phone outreach)

• Use plain-language screening questions (no jargon, no self-rating scales)

• Adapt methods: moderated-only testing, shorter sessions (30-40 min), read tasks aloud

• Measure: unassisted task completion (>=80%), time-to-first-value (<2 min), error recovery rate

• Frame findings as "inclusion improvements," not "dumbing down"

• Cross-reference with simplification audit template

Measurement & Decision Quality (Core)

Research ROI Quick Reference

Research Activity Proxy Metric Calculation

Usability testing finding Prevented dev rework Hours saved × $150/hr

Discovery interview Prevented build-wrong-thing Sprint cost × risk reduction %

A/B test conclusive result Improved conversion (ΔConversion × Traffic × LTV) - Test cost

Heuristic evaluation Early defect detection Defects 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

When NOT to Run A/B Tests

Situation Why it fails Better method

Low power/traffic Inconclusive results Usability tests + trends

Many variables change Attribution impossible Prototype tests → staged rollout

Need “why” Experiments don’t explain Interviews + observation

Ethical constraints Harmful denial Phased rollout + holdouts

Long-term effects Short tests miss delayed impact Longitudinal + retention analysis

Common Confounds (Call Out Early)

Always check for these in experiment evaluations. List each relevant confound with its risk level and how to verify — do not just name them:

• Selection bias (only power users respond) — check segment composition.

• Survivorship bias (you miss churned users) — compare with cohort-level data.

• Novelty effect (short-term lift) — plot daily metrics to check for trend decay.

• Instrumentation changes mid-test (metrics drift) — confirm no concurrent deployments.

• Sample ratio mismatch (SRM) — run chi-square on assignment counts.

• Peeking / multiple looks — confirm test was not checked before pre-set end date.

• Feature interaction — check if other experiments ran concurrently on same surface.

Optional: AI/Automation Research Considerations

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

2026 benchmark: Trend reports consistently highlight AI-assisted analysis. Use AI for speed while keeping humans responsible for strategy and interpretation. Example reference: https://www.lyssna.com/blog/ux-research-trends/

Key Questions

Dimension Question Methods

Mental model What do users think the system can/can’t do? Interviews, concept tests

Trust calibration When do users over/under-rely? Scenario tests, log review

Explanation usefulness Does “why” help decisions? A/B explanation variants, interviews

Failure recovery Do users recover and finish tasks? Failure-path usability tests

Error Taxonomy (User-Visible)

Failure type Typical impact What to measure

Wrong output Rework, lost trust Verification + override rate

Missing output Manual fallback Fallback completion rate

Unclear output Confusion Clarification requests

Non-recoverable failure Blocked flow Time-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)

Trend reports frequently mention synthetic/AI participants. Use with clear boundaries. Example reference: https://www.lyssna.com/blog/ux-research-trends/

Use Case Appropriate? Why

Early concept brainstorming WARNING: Supplement only Generate edge cases, not validation

Scenario/edge case expansion PASS Yes Broaden coverage before real testing

Moderator training/practice PASS Yes Practice without participant burden

Hypothesis generation PASS Yes Explore directions to test with real users

Validation/go-no-go decisions FAIL Never Cannot substitute lived experience

Usability findings as evidence FAIL Never Real behavior required

Quotes in reports FAIL Never Fabricated quotes damage credibility

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

Navigation

Resources

Core Research Methods:

• references/research-frameworks.md — JTBD, Kano, Double Diamond, Service Blueprint, opportunity mapping

• references/ux-audit-framework.md — Heuristic evaluation, cognitive walkthrough, severity rating

• references/usability-testing-guide.md — Task design, facilitation, analysis

• references/ux-metrics-framework.md — Task metrics, SUS/HEART, measurement guidance

• references/customer-journey-mapping.md — Journey mapping and service blueprints

• references/pain-point-extraction.md — Feedback-to-themes method

• references/review-mining-playbook.md — B2B/B2C review mining

Demographic & Quantitative Research:

• references/demographic-research-methods.md — Inclusive research for seniors, children, cultures, disabilities

• references/non-technical-user-research.md — Research methods for non-technical and low-digital-literacy users

• references/ab-testing-implementation.md — A/B testing deep-dive (sample size, analysis, pitfalls)

Competitive UX Analysis & Flow Patterns:

• references/competitive-ux-analysis.md — Step-by-step flow patterns from industry leaders (Wise, Revolut, Shopify, Notion, Linear, Stripe) + benchmarking methodology

Research Operations & Methods:

• references/research-repository-management.md — Repository architecture, taxonomy, atomic research, PII handling, adoption metrics

• references/survey-design-guide.md — Question types, bias prevention, sampling, sample size, distribution, platform comparison

• references/remote-research-patterns.md — Moderated remote, unmoderated testing, async methods, recruitment, tool comparison

Feedback Collection & Analysis:

• references/bigtech-feedback-patterns.md — How top companies collect and act on user feedback

• references/feedback-tools-guide.md — Feedback collection tool setup guides and selection matrix

Evaluative Iteration:

• references/evaluative-research-loop.md — Prototype-parity polishing loop (two-surface audit, drift classification, fast iteration)

Data & Sources:

• data/sources.json — Curated external references

Domain-Specific UX Benchmarking

IMPORTANT: When designing UX flows for a specific domain, you MUST use WebSearch to find and suggest best-practice patterns from industry leaders.

Trigger Conditions

• "We're designing [flow type] for [domain]"

• "What's the best UX for [feature] in [industry]?"

• "How do [Company A, Company B] handle [flow]?"

• "Benchmark our [feature] against competitors"

• Any UX design task with identifiable domain context

Domain → Leader Lookup Table

Domain Industry Leaders to Check Key Flows

Fintech/Banking Wise, Revolut, Monzo, N26, Chime, Mercury Onboarding/KYC, money transfer, card management, spend analytics

E-commerce Shopify, Amazon, Stripe Checkout Checkout, cart, product pages, returns

SaaS/B2B Linear, Notion, Figma, Slack, Airtable Onboarding, settings, collaboration, permissions

Developer Tools Stripe, Vercel, GitHub, Supabase Docs, API explorer, dashboard, CLI

Consumer Apps Spotify, Airbnb, Uber, Instagram Discovery, booking, feed, social

Healthcare Oscar, One Medical, Calm, Headspace Appointment booking, records, compliance flows

EdTech Duolingo, Coursera, Khan Academy Onboarding, progress, gamification

Required Searches

When user specifies a domain, execute:

• Search: "[domain] UX best practices 2026"

• Search: "[leader company] [flow type] UX"

• Search: "[leader company] app review UX" site:mobbin.com OR site:pageflows.com

• Search: "[domain] onboarding flow examples"

What to Report

After searching, provide:

• Pattern examples: Screenshots/flows from 2-3 industry leaders

• Key patterns identified: What they do well (with specifics)

• Applicable to your flow: How to adapt patterns

• Differentiation opportunity: Where you could improve on leaders

Example Output Format

DOMAIN: Fintech (Money Transfer) BENCHMARKED: Wise, Revolut

WISE PATTERNS:

• Upfront fee transparency (shows exact fee before recipient input)
• Mid-transfer rate lock (shows countdown timer)
• Delivery time estimate per payment method
• Recipient validation (bank account check before send)

REVOLUT PATTERNS:

• Instant send to Revolut users (P2P first)
• Currency conversion preview with rate comparison
• Scheduled/recurring transfers prominent

APPLY TO YOUR FLOW:

1. Add fee transparency at step 1 (not step 3)
2. Show delivery estimate per payment rail
3. Consider rate lock feature for FX transfers

DIFFERENTIATION OPPORTUNITY:

• Neither shows historical rate chart—add "is now a good time?" context

Trend Awareness Protocol

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

Tool/Trend Triggers

• "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]?"

Tool/Trend Searches

• Search: "UX research trends 2026"

• Search: "UX research tools best practices 2026"

• Search: "[Maze/Hotjar/UserTesting] comparison 2026"

• Search: "AI in UX research 2026"

Tool/Trend Report Format

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

Example Topics (verify with fresh search)

• 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

• Shared plan template: ../software-clean-code-standard/assets/checklists/ux-research-plan-template.md — Product-agnostic research plan template (core + optional AI)

• assets/research-plan-template.md — UX research plan template

• assets/testing/usability-test-plan.md — Usability test plan

• assets/testing/usability-testing-checklist.md — Usability testing checklist

• assets/audits/heuristic-evaluation-template.md — Heuristic evaluation

• assets/audits/ux-audit-report-template.md — Audit report

Evaluative Research Loop

For prototype-parity polishing (fast iteration when product is "almost ideal"), see references/evaluative-research-loop.md. Covers: two-surface audit, drift classification (layout/density/control/content/state), friction-based prioritization, banner/loading guardrails, localization-readiness checks, and fast iteration cadence.

Fact-Checking

• Use web search/web fetch to verify current external facts, versions, pricing, deadlines, regulations, or platform behavior before final answers.

• Prefer primary sources; report source links and dates for volatile information.

• If web access is unavailable, state the limitation and mark guidance as unverified.