software-ux-research

📁 vasilyu1983/ai-agents-public 📅 Jan 23, 2026
29
总安装量
29
周安装量
#7096
全站排名
安装命令
npx skills add https://github.com/vasilyu1983/ai-agents-public --skill software-ux-research

Agent 安装分布

claude-code 17
cursor 16
gemini-cli 15
codex 14
antigravity 12

Skill 文档

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 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):


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)

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.

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

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

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.

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:

Demographic & Quantitative Research (NEW):

Competitive UX Analysis & Flow Patterns:

Data & Sources:


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:

  1. Search: "[domain] UX best practices 2026"
  2. Search: "[leader company] [flow type] UX"
  3. Search: "[leader company] app review UX" site:mobbin.com OR site:pageflows.com
  4. 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

  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"

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