Agent skill

software-ux-research

UX research and analysis for discovering, validating, and evaluating software experiences, including research ops, governance, and measurement.

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SKILL.md

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.

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/templates/checklists/ux-research-plan-template.md; use templates/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)

Method Chooser (Core)

Research Types (Keep Explicit)

Type Goal Primary Outputs
Discovery Understand needs and context JTBD, opportunity areas, constraints
Validation Reduce solution risk Go/no-go, prioritization signals
Evaluative Improve usability/accessibility Severity-rated issues + fixes

Decision Tree (Fast)

text
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)

Question Best methods Avoid when Output
What problems matter most? Interviews, contextual inquiry, diary Only surveys/analytics Problem framing + evidence
Can users complete key tasks? Moderated usability tests, task analysis Stakeholder review Task success + issue list
Is navigation findable? Tree test, first-click, card sort Extremely small audience [Inference] IA changes + labels
What is happening at scale? Funnels, cohorts, logs, support taxonomy Instrumentation missing Baselines + segments + drop-offs
Which variant performs better? A/B, switchback, holdout Insufficient power or high risk Decision with confidence + guardrails

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
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

Post-Launch Measurement (What to Track)

Metric category What it answers Pair with
Adoption Are people using it? Outcome/value metric
Value Does it help users succeed? Adoption + qualitative reasons
Reliability Does it fail in ways users notice? Error rate + recovery success
Accessibility Can diverse users complete flows? Assistive-tech coverage + defect trends

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.

Measurement & Decision Quality (Core)

Triangulation Rubric

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

Adoption vs Value (Avoid Vanity Metrics)

Metric type Example Common pitfall
Adoption Feature usage rate “Used” ≠ “helpful”
Value/outcome Task success, goal completion Harder to instrument

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)

  • 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.

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.
  • Prohibit fabricated quotes and “synthetic users” as evidence for real decisions.

Navigation

Resources

  • resources/research-frameworks.md — JTBD, Kano, Double Diamond, Service Blueprint, opportunity mapping
  • resources/ux-audit-framework.md — Heuristic evaluation, cognitive walkthrough, severity rating
  • resources/usability-testing-guide.md — Task design, facilitation, analysis
  • resources/ux-metrics-framework.md — Task metrics, SUS/HEART, measurement guidance
  • resources/customer-journey-mapping.md — Journey mapping and service blueprints
  • resources/pain-point-extraction.md — Feedback-to-themes method
  • resources/review-mining-playbook.md — B2B/B2C review mining
  • data/sources.json — Curated external references

Templates

  • Shared plan template: ../software-clean-code-standard/templates/checklists/ux-research-plan-template.md — Product-agnostic research plan template (core + optional AI)
  • templates/research-plan-template.md — UX research plan template
  • templates/testing/usability-test-plan.md — Usability test plan
  • templates/testing/usability-testing-checklist.md — Usability testing checklist
  • templates/audits/heuristic-evaluation-template.md — Heuristic evaluation
  • templates/audits/ux-audit-report-template.md — Audit report

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