Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add memi-design/design-skills --skill usability-testinggit clone --depth 1 https://github.com/memi-design/design-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/memi-design/design-skills/usability-testing)<a href="https://agentmods.dev/skills/memi-design/design-skills/usability-testing"><img src="https://agentmods.dev/badge/skills/memi-design/design-skills/usability-testing/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/memi-design/design-skills/usability-testing"><img src="https://agentmods.dev/badge/skills/memi-design/design-skills/usability-testing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00033 | $0.02546 |
| Opus 5.5 | $0.00013 | $0.01018 |
| Sonnet 5.5 | $0.00007 | $0.00509 |
| Haiku 4.5 | $0.00003 | $0.00255 |
Grade A, and why
usability-testing scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 319 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
| Method | Best For | Participants | Duration |
|---|---|---|---|
| Moderated in-person | Complex flows, emotional reactions | 5-8 | 45-60 min |
| Moderated remote | Geographic diversity, screen sharing | 5-8 | 30-45 min |
| Unmoderated remote | High volume, simple tasks | 15-30 | 10-20 min |
| Guerrilla | Quick validation, low-fidelity prototypes | 5-10 | 5-15 min |
| A/B comparison | Comparing two design variants | 10-20 per variant | 15-30 min |
3. Determine Fidelity
| Fidelity | Tool | When to Use |
|---|---|---|
| Paper / wireframe | Sketches, Balsamiq | Early concept validation |
| Mid-fidelity | Figma prototype (no animation) | Flow and IA validation |
| High-fidelity | Figma prototype (full interactions) | Visual design and micro-interaction testing |
| Production | Staged deployment | Pre-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
- Write scenarios, not instructions. Say "Find a book under $30" not "Click the search bar, type books, click filter."
- Avoid leading language. Do not mention UI elements by name.
- Include realistic context. Why is the user doing this?
- Define clear completion criteria. What state means success?
- Order tasks from simple to complex. Build confidence before testing harder flows.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 319 lines · 33 tokens per session scan A 31622277521d
usability-testing is a skill published in the GitHub repository memi-design/design-skills (7 stars, last pushed 29d ago), licensed MIT. It adds 33 tokens to every session and 2,546 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
theme-audit
Audit theme parity: tokens missing or unchanged per theme, component tokens bypassing semantics, contrast within each theme, resolver contexts, theme-switch regressions. Triggers: dark mode audit, theme coverage, brand variant parity. For general token structure use token-audit.
token-audit
Audit how design tokens are defined: tiers, naming, alias chains, raw values, orphans, DTCG readiness. Triggers: audit my tokens, token architecture review, token health check. Not for code consuming tokens (token-compliance), theme parity (theme-audit) or Figma variables (figma-variable-audit).
accessibility-per-component
Audit one design system component's accessibility against WCAG 2.2 AA: keyboard, screen reader, contrast, focus, ARIA, target size. Trigger: a11y audit, is this accessible, WCAG check, screen reader support, keyboard navigation check. Not for page- or product-wide audits.
ai-component-description
Write a six-section prose description (purpose, props, anti-patterns, composition, accessibility, examples) for a Figma component's description field so LLMs read it via MCP. Triggers: describe this component for AI, Figma MCP description. JSON metadata files: use metadata-schema-generator.
codebase-index
Generate machine-readable index files in .ai/index/ (component inventory, uses/usedBy graph, stats) for AI agents. Triggers: index my codebase, build a relationship graph, what depends on what. Not an assessment; for library health use component-audit.
component-decision-tree
Write "choosing between" pages (docs/choosing/) that route an intent to the right component via narrowing questions; YAML trees on request. Triggers: which component should I use, choose between X and Y, dialog vs drawer, selection guide. One chosen component: usage-guidelines.