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Usability Testing

Organization
memi-design
usability-testing

Usability testing frameworks — test planning, task scenarios, moderated and unmoderated protocols, analysis frameworks, and insight synthesis for Mémoire research pipeline

Overview

Publishermemi-design
Repositorydesign-skills
Skill nameusability-testing
Stars
7
Forks
0
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by memi-design on GitHub. Read the source before you install it.

Installation

Install the Usability Testing AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/memi-design/design-skills/tree/main/skills/usability-testing
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/memi-design/design-skills.git /tmp/design-skills
mkdir -p .claude/skills
cp -r /tmp/design-skills/skills/usability-testing .claude/skills/usability-testing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Usability Testing in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Usability Testing on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Usability Testing is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Usability Testing -- Research Skill Pack

Structured methodology for planning, executing, and analyzing usability tests. Integrates with the Memoire research pipeline to transform raw observations into actionable design decisions.


Test Planning Methodology

1. Define Research Questions

Every test starts with 3-5 focused research questions. Format:

RQ1: Can users complete [task] without assistance?
RQ2: Where do users hesitate or make errors during [flow]?
RQ3: How does [new pattern] compare to [existing pattern] in efficiency?

2. Choose Test Type

MethodBest ForParticipantsDuration
Moderated in-personComplex flows, emotional reactions5-845-60 min
Moderated remoteGeographic diversity, screen sharing5-830-45 min
Unmoderated remoteHigh volume, simple tasks15-3010-20 min
GuerrillaQuick validation, low-fidelity prototypes5-105-15 min
A/B comparisonComparing two design variants10-20 per variant15-30 min

3. Determine Fidelity

FidelityToolWhen to Use
Paper / wireframeSketches, BalsamiqEarly concept validation
Mid-fidelityFigma prototype (no animation)Flow and IA validation
High-fidelityFigma prototype (full interactions)Visual design and micro-interaction testing
ProductionStaged deploymentPre-launch validation

Task Scenario Design

Anatomy of a Good Task

Context:  You are shopping for a birthday gift for a friend.
Trigger:  You want to find a book under $30 and add it to your cart.
Goal:     Complete the purchase using the saved payment method.

Rules

  1. Write scenarios, not instructions. Say "Find a book under $30" not "Click the search bar, type books, click filter."
  2. Avoid leading language. Do not mention UI elements by name.
  3. Include realistic context. Why is the user doing this?
  4. Define clear completion criteria. What state means success?
  5. Order tasks from simple to complex. Build confidence before testing harder flows.

Task Template

FieldContent
Task IDT-001
Scenario[Context + trigger + goal]
Success Criteria[Observable outcome]
Max Time[Minutes before intervention]
Priority[Critical / Important / Nice-to-have]
Related RQ[RQ1, RQ2, etc.]

Moderated Testing Protocol

Before the Session

  1. Pilot test with 1 internal participant -- fix unclear tasks
  2. Prepare consent form (recording, data usage)
  3. Set up recording (screen + audio minimum; camera optional)
  4. Prepare facilitator guide with exact script

Facilitator Guide Structure

1. Welcome and consent          (2 min)
2. Background questions         (3 min)
3. Task scenarios               (25-35 min)
4. Post-task questionnaire      (5 min)
5. Debrief and open questions   (5 min)

Facilitation Rules

  • Use the think-aloud protocol: "Tell me what you are thinking as you go."
  • Never answer questions about the interface. Redirect: "What would you expect to happen?"
  • Note moments of confusion, hesitation, backtracking, and verbal frustration.
  • Record timestamps for each task start and task completion.
  • If participant is stuck for more than 2 minutes, offer a hint. After 3 minutes, assist and note the failure.

Unmoderated Testing Protocol

Platform Setup

Configure in tools such as UserTesting, Maze, or Lookback:

  1. Welcome screen with consent and instructions
  2. Screener questions to validate participant fit
  3. Task sequence with success URLs or click targets
  4. Post-task single ease question (SEQ) after each task
  5. Final SUS questionnaire
  6. Thank-you screen with compensation details

Task Validation

  • Define success paths (correct click sequence)
  • Define failure indicators (wrong page, timeout, abandon)
  • Set maximum task duration
  • Include at least one baseline task that should be trivially easy

Metrics

Core Metrics

MetricFormulaTarget
Task Success Rate(Successful completions / Total attempts) * 100> 80%
Time on TaskMedian seconds from task start to completionContext-dependent
Error RateErrors per task per participant< 1.0
Lostness(N/S - 1) + (R/N - 1) where N=pages visited, S=minimum path, R=revisits< 0.4
Task-Level SatisfactionSingle Ease Question (SEQ), 1-7 scale> 5.0

Standardized Questionnaires

InstrumentMeasuresScaleBenchmark
SUS (System Usability Scale)Overall usability0-100> 68 is above average
SEQ (Single Ease Question)Per-task difficulty1-7> 5.0 is acceptable
UMUX-LiteUsability + usefulness0-100> 65 is acceptable
NASA-TLXCognitive workload0-100 (lower is better)Context-dependent

SUS Calculation

1. For odd-numbered questions (1,3,5,7,9): score - 1
2. For even-numbered questions (2,4,6,8,10): 5 - score
3. Sum all adjusted scores
4. Multiply by 2.5
5. Result is 0-100

Recruitment and Sampling

Sample Size Guidelines

GoalParticipantsRationale
Find major usability issues5Discovers ~85% of problems (Nielsen/Landauer)
Quantitative confidence20+Statistical significance for task metrics
A/B comparison15-20 per variantEnough for between-subjects comparison
Accessibility audit3-5 per disability typeSpecialized needs require targeted recruitment

Screener Design

  1. Demographics (age, location, occupation)
  2. Technology proficiency (devices, frequency)
  3. Domain experience (relevant product/service usage)
  4. Disqualifiers (employees, competitors, recent participants)
  5. Availability and compensation agreement

Recruitment Sources

  • User database / CRM (existing users)
  • Panel services (UserTesting, Respondent, Prolific)
  • Social media and community channels
  • Intercept / pop-up on live product
  • Internal team members (pilot only, never for real data)

Analysis Frameworks

Rainbow Spreadsheet

ParticipantTaskObservationSeverityTheme
P1T-001Could not find filter buttonHighDiscoverability
P2T-001Found filter but expected different behaviorMediumMental model mismatch
P3T-002Completed easily----

Color-code rows by participant. Cluster observations by theme. Count frequency across participants.

Severity Rating

LevelLabelDefinition
1CosmeticNoticed but no impact on task completion
2MinorSlows users down but they recover
3MajorCauses errors or significant confusion; some fail
4CriticalBlocks task completion for most users

Affinity Mapping

  1. Write each observation on a virtual sticky (Figma, FigJam, or Miro)
  2. Group stickies by similarity without pre-defined categories
  3. Name each group with a theme label
  4. Rank themes by frequency and severity
  5. Map themes to design recommendations

Report Template

markdown
# Usability Test Report: [Feature/Product Name]

## Executive Summary
- [2-3 sentence overview of findings]
- [Key metric: e.g., "Task success rate was 65%, below the 80% target"]

## Methodology
- Participants: [N], recruited via [source]
- Method: [Moderated remote / Unmoderated / etc.]
- Date: [Range]
- Tasks: [Count]

## Key Findings

### Finding 1: [Theme Name]
- Severity: [Critical / Major / Minor / Cosmetic]
- Frequency: [X of Y participants]
- Evidence: [Quote or observation]
- Recommendation: [Specific design change]

### Finding 2: ...

## Metrics Summary
| Task | Success Rate | Median Time | Error Rate | SEQ |
|------|-------------|-------------|------------|-----|
| T-001 | 80% | 45s | 0.4 | 5.2 |
| T-002 | 60% | 120s | 1.8 | 3.1 |

## SUS Score: [XX] ([Adjective rating])

## Recommendations (Prioritized)
1. [Critical fix] -- Expected impact: [metric improvement]
2. [Major improvement] -- Expected impact: [metric improvement]
3. [Minor enhancement] -- Expected impact: [metric improvement]

## Appendix
- Task scenarios
- Screener questionnaire
- Raw data tables

Integration with Memoire Research Pipeline

Data Flow

Usability test data (Excel/CSV/Stickies)
  --> memi research from-file <data>
  --> memi research synthesize
  --> memi research report
  --> Dashboard / Figma stickies

Supported Input Formats

FormatCommandNotes
Excel spreadsheetmemi research from-file results.xlsxRainbow spreadsheet format
CSV exportmemi research from-file results.csvFrom Maze, UserTesting
Figma stickiesmemi research from-stickiesAffinity map in FigJam
Manual notesmemi research from-file notes.mdMarkdown observation log

Synthesis Pipeline

  1. Import: Parse raw data into structured observations
  2. Classify: Tag each observation by severity, task, and theme
  3. Cluster: Group observations into findings using affinity analysis
  4. Quantify: Calculate metrics (success rate, error rate, SUS)
  5. Prioritize: Rank findings by severity * frequency
  6. Report: Generate markdown report and dashboard components

Remote Testing Tools and Setup

Tool Comparison

ToolModeratedUnmoderatedPrototype TestingAnalyticsPrice Tier
UserTestingYesYesYesAdvancedEnterprise
MazeNoYesYes (Figma)GoodMid
LookbackYesYesLimitedBasicMid
HotjarNoSurveys onlyNoHeatmapsLow
Lyssna (UsabilityHub)NoYesFirst-click, preferenceBasicLow

Remote Session Checklist

  • Confirm participant has stable internet and a quiet environment
  • Test screen-sharing and recording before the session
  • Share prototype link (not the Figma editor link)
  • Disable notifications on facilitator and participant machines
  • Have a backup communication channel (phone number)
  • Record both screen and audio; get consent before starting
  • Prepare a post-session backup plan if technology fails

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Usability Testing AI skill do?

Usability testing frameworks — test planning, task scenarios, moderated and unmoderated protocols, analysis frameworks, and insight synthesis for Mémoire research pipeline

Why use Usability Testing on TypingMind?

Because you install it once and use it with any model. Usability Testing is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Usability Testing in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/memi-design/design-skills/tree/main/skills/usability-testing. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Usability Testing?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Usability Testing?

As many as you like. As long as a model supports skills, you can use Usability Testing with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Usability Testing AI skill free?

Yes. It is published on GitHub by memi-design under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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