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