Owl-Listener/designer-skills is a collection of AI-agent skills, commands, and plugins for design work, covering research, design systems, interfaces, interaction, and delivery. Designers and developers use it inside coding assistants to guide design tasks, and the catalogue entries represent selected parts of that larger collection.
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 Owl-Listener/designer-skills --skill aesthetic-usabilitygit clone --depth 1 https://github.com/Owl-Listener/designer-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/owl-listener/designer-skills/aesthetic-usability)<a href="https://agentmods.dev/skills/owl-listener/designer-skills/aesthetic-usability"><img src="https://agentmods.dev/badge/skills/owl-listener/designer-skills/aesthetic-usability/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/owl-listener/designer-skills/aesthetic-usability"><img src="https://agentmods.dev/badge/skills/owl-listener/designer-skills/aesthetic-usability.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00059 | $0.00510 |
| Opus 5 | $0.00030 | $0.00255 |
| Sonnet 5 | $0.00012 | $0.00102 |
| Haiku 4.5 | $0.00006 | $0.00051 |
Grade A, and why
aesthetic-usability 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 8d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- aesthetic-usability — 91% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 32 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Aesthetic-Usability Effect
You are an expert in the relationship between visual quality and perceived usability.
What You Do
You apply the Aesthetic-Usability Effect to ensure visual consistency and polish translate into user trust and perceived quality — without masking genuine usability problems.
The Principle
Users perceive aesthetically pleasing interfaces as easier to use, even before interacting with them. This is not about decoration — it is about consistency as a signal of quality:
- Consistent spacing, alignment, and type scale signals that the product is well-considered
- Visual noise or inconsistency makes users doubt the reliability of the system
- A polished surface creates tolerance: users forgive minor friction in beautiful UIs more readily
Where It Applies
- First impressions: onboarding, landing pages, empty states — users form opinions before first interaction
- Error states: a well-designed error screen reads as trustworthy; a rough one reads as broken
- Trust-critical contexts: payment flows, health data, legal content — aesthetics directly affect willingness to proceed
- Design systems: consistent component usage signals quality across the entire product
The Risk
The effect can mask usability problems. A beautiful interface that is hard to use will eventually frustrate users — aesthetic tolerance has limits. Use it to lower the bar for first impressions, not to substitute for sound information architecture or interaction design.
Applying It
- Establish and enforce a consistent spacing and type scale — irregularity reads as carelessness
- Align to grid; misaligned elements signal low craft even if functional
- Maintain visual weight consistency across similar actions (buttons, links, icons)
- Design error, empty, and loading states with the same care as primary flows
- Audit for visual inconsistency before launch — a single rough screen can lower the perceived quality of surrounding screens
Best Practices
- Consistency is the most reliable aesthetic signal — prioritize it over novelty
- Test perceived quality with users who haven't seen the design before
- Don't confuse visual complexity with quality; restrained, deliberate design reads as more polished
- Pair aesthetic investment with usability testing — polish should not substitute for structural clarity
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.
- 8d ago First seen · 32 lines · 59 tokens per session scan A d0b6b6c392b5
aesthetic-usability is a skill published in the GitHub repository Owl-Listener/designer-skills (2,609 stars, last pushed 5d ago), licensed MIT. It adds 59 tokens to every session and 510 once invoked, about $0.0003 per session on Opus 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
ui-review
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design-system
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accessibility-a11y
Semantic HTML, keyboard navigation, focus states, ARIA labels, skip links, and WCAG contrast requirements. Use when ensuring accessibility compliance, implementing keyboard navigation, or adding screen reader support.
tailwind-shadcn
Tailwind CSS utility patterns with shadcn/ui component usage, theming via CSS variables, and responsive design. Use when styling components, installing shadcn components, implementing dark mode, or creating consistent design systems.
anti-slop-frontend
A mechanical, countable anti-slop checklist for AI-generated frontend. Catches the specific signatures an undirected model defaults to: AI-purple glows, Inter-everywhere, em-dashes, div-based fake screenshots, eyebrow-on-every-section, beige+brass "premium" palettes, generic Jane Doe / Acme data. Advisory layer that…
frontend-mockup-loop-dashboard
Dashboard-specific adapter on the generic frontend-mockup-loop skill: binds the 7-step design loop to pi-agent-dashboard component sources, theme-system tokens, and isolated verification. Use when designing/redesigning any pi-agent-dashboard client surface. Triggers: "design a dashboard screen", "mockup a dashboard…