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 Infrasity-Labs/dev-gtm-claude-skills --skill aesthetic-usabilitygit clone --depth 1 https://github.com/Infrasity-Labs/dev-gtm-claude-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/infrasity-labs/dev-gtm-claude-skills/aesthetic-usability)<a href="https://agentmods.dev/skills/infrasity-labs/dev-gtm-claude-skills/aesthetic-usability"><img src="https://agentmods.dev/badge/skills/infrasity-labs/dev-gtm-claude-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/infrasity-labs/dev-gtm-claude-skills/aesthetic-usability"><img src="https://agentmods.dev/badge/skills/infrasity-labs/dev-gtm-claude-skills/aesthetic-usability.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 | $0.00025 | $0.00476 |
| Opus 5.5 | $0.00010 | $0.00190 |
| Sonnet 5.5 | $0.00005 | $0.00095 |
| Haiku 4.5 | $0.00003 | $0.00048 |
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.
This is a copy
91% identical to aesthetic-usability — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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 · 25 tokens per session scan A 26a0adecab82
aesthetic-usability is a skill published in the GitHub repository Infrasity-Labs/dev-gtm-claude-skills (148 stars, last pushed 4mo ago), licensed MIT. It adds 25 tokens to every session and 476 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 91% identical to aesthetic-usability, differing in 2 lines, and is treated as a copy.
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