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 5.5 | $0.00024 | $0.00204 |
| Sonnet 5.5 | $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 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.
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.
- 1mo 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 (3,137 stars, last pushed 1mo ago), licensed MIT. It adds 59 tokens to every session and 510 once invoked, about $0.0002 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
ui-review
Review UI code for StyleSeed design-system compliance, accessibility, mobile ergonomics, spacing discipline, and implementation quality.
design-craft
Design or substantially improve user-facing interfaces with a product-specific visual direction, content hierarchy, and rendered critique. Use for new screens, landing pages, redesigns, typography/layout work, or UI that feels generic or AI-generated. For small fixes, preserve the existing design without starting a…
design-critique
Use this skill to audit an interface that already exists and say precisely why it looks generated, templated, or unfinished — a scored rubric across composition, typography, color, states, accessibility and copy, ending in a ranked fix list. Triggers: user says "review the design", "critique this UI", "why does this…
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…
design-reference-read
Read a reference website or screenshot the way a designer does — by measuring it. Produces a dated "design DNA" record (color roles, type scale and ratio, spacing unit, radii, elevation, motion, section rhythm per viewport, component geometry, declared tokens) with measured and inferred values kept apart, names the…
design-to-code
Implement a supplied Figma design, screenshot, live reference site or design specification as a working interface, and prove the match with measured fidelity scores per axis (color, type, space, shape, layout, motion) instead of eyeballing. Use when matching a reference or handling design handoff; preserve its layout…