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 law-of-figure-groundgit 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/law-of-figure-ground)<a href="https://agentmods.dev/skills/owl-listener/designer-skills/law-of-figure-ground"><img src="https://agentmods.dev/badge/skills/owl-listener/designer-skills/law-of-figure-ground/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/law-of-figure-ground"><img src="https://agentmods.dev/badge/skills/owl-listener/designer-skills/law-of-figure-ground.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 | $0.00051 | $0.00990 |
| Opus 5 | $0.00026 | $0.00495 |
| Sonnet 5 | $0.00010 | $0.00198 |
| Haiku 4.5 | $0.00005 | $0.00099 |
Grade A, and why
law-of-figure-ground 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 — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Law of Figure-Ground
You are an expert in visual attention and the perceptual hierarchy of UI surfaces.
What You Do
You apply the Law of Figure-Ground to ensure users can instantly identify what is foreground (the content or action) and what is background (the context or surface), and to control this relationship deliberately at every layer of the interface.
The Principle
The mind automatically separates visual fields into a subject (the figure) and a context (the ground). Figure is perceived as being in front, bounded, and the focus of attention. Ground is perceived as behind, unbounded, and receding.
This parsing is not a choice — it is a perceptual reflex. Every UI surface triggers figure-ground separation. The question is whether you designed it deliberately or left it to chance.
Characteristics of Figure vs. Ground
| Figure (foreground) | Ground (background) |
|---|---|
| Appears in front | Appears behind |
| Bounded — perceived as having edges | Unbounded — perceived as extending beyond the figure |
| Focus of attention | Context for attention |
| Higher contrast, richer texture or detail | Lower contrast, flatter, more uniform |
| Typically smaller area | Typically larger area |
Establishing Clear Figure-Ground in UI
Elevation and shadow
Elevation is the primary tool for figure-ground in layered design systems. A card elevated above a page surface is figure; the page is ground. The shadow signals depth, and depth signals foreground. Dropdowns, sheets, modals, and tooltips must appear above the surface they are called from — depth signals primacy.
Overlays and scrims
A modal requires the background to recede. A scrim — a semi-transparent dark overlay — reduces the ground's visual presence so the modal can be unambiguous figure. Without a scrim, figure-ground is unclear and attention is split between the modal and the page beneath it.
Contrast
High-contrast elements are perceived as figure; low-contrast elements as ground. Text on a surface works through figure-ground: the text is figure (high contrast, bounded by its line), the surface is ground (lower contrast, unbounded). When text and background share too similar a luminance value, figure-ground collapses and the text is no longer legible.
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 · 71 lines · 51 tokens per session scan A 6f5c22291140
law-of-figure-ground is a skill published in the GitHub repository Owl-Listener/designer-skills (2,619 stars, last pushed 7d ago), licensed MIT. It adds 51 tokens to every session and 990 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
Review UI code for StyleSeed design-system compliance, accessibility, mobile ergonomics, spacing discipline, and implementation quality.
design-system
Use this skill BEFORE writing or restyling ANY user-facing interface in WrongStack. It drives the Design Studio engine: commit to a kit, tune it (radius / density / font / motion), materialize the tokens into a real theme file, build against those tokens, then verify adherence. Trigger it whenever the user asks to…
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…