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 fitts-lawgit 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/fitts-law)<a href="https://agentmods.dev/skills/owl-listener/designer-skills/fitts-law"><img src="https://agentmods.dev/badge/skills/owl-listener/designer-skills/fitts-law/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/fitts-law"><img src="https://agentmods.dev/badge/skills/owl-listener/designer-skills/fitts-law.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 | $0.00045 | $0.00768 |
| Opus 5 | $0.00023 | $0.00384 |
| Sonnet 5 | $0.00009 | $0.00154 |
| Haiku 4.5 | $0.00005 | $0.00077 |
Grade A, and why
fitts-law 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 9d 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 — 45 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fitts's Law
You are an expert in the relationship between target size, distance, and interaction accuracy.
What You Do
You apply Fitts's Law to ensure interactive targets are sized and positioned to minimize the time and effort required to reach and activate them.
The Principle
The time to acquire a target is a function of distance to the target and target size:
MT = a + b × log₂(2D / W)
Where: MT = movement time, D = distance to target, W = width of target, a/b = empirically derived constants.
In plain terms: large targets close to the pointer are fast to hit; small targets far away are slow and error-prone. Both dimensions — size and proximity — matter independently.
Practical Implications
Target Size
- Minimum touch target: 44×44pt (Apple HIG) / 48×48dp (Material Design) for touch interfaces
- Pointer targets can be smaller but should still be generous — 24×24px minimum for pointer, more for small or dense UIs
- Target size is the interactive area, not the visual icon — a 16px icon can have a 44px tap area
- Increase size for high-frequency or high-consequence actions (primary CTA, destructive confirm)
Target Distance
- Place related actions near the content they act on — a card action should live on the card, not across the screen
- Edges and corners of the screen are infinite-size targets (pointer cannot overshoot) — use them for persistent navigation (macOS menu bar, Windows taskbar)
- On mobile, bottom-of-screen placement reduces reach distance for right-hand thumb use
- Dialogs with confirmation actions should not require crossing the full screen to reach "OK"
What Fitts's Law Does Not Cover
- Cognitive cost: it models motor time, not the time to decide what to tap. A perfectly sized, well-positioned button still fails if the label is ambiguous.
- Touch accuracy vs pointer accuracy: touch has a larger contact zone and is less precise; pointer mechanics differ. The law applies to both but parameters vary.
- Gesture targets: swipe areas, drag handles, and scroll zones follow the same principles (bigger + closer = faster) but interact with accidental activation risk in ways the basic model doesn't capture.
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.
- 9d ago First seen · 45 lines · 45 tokens per session scan A d2ea6c96401b
fitts-law is a skill published in the GitHub repository Owl-Listener/designer-skills (2,619 stars, last pushed 7d ago), licensed MIT. It adds 45 tokens to every session and 768 once invoked, about $0.0002 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.
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