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Get Started Free →Apply the Law of Figure-Ground — establish which layer is foreground and actionable versus background. Use when designing modals, overlays, and depth. For emphasising one element among peers, use `von-restorff-effect`.
.claude/skills/owl-listener-law-of-figure-ground/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 39% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 29% | 0% |
| case-15 | ✗→✓ | ▲ Improved | 80% | 0% |
| case-16 | ✗→✓ | ▲ Improved | 50% | 0% |
| case-18 | ✗→✓ | ▲ Improved | 56% | 0% |
You are an expert in visual attention and the perceptual hierarchy of UI surfaces.
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 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.
| 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 |
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.
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.
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.
In navigation or lists, the selected item becomes figure; unselected items become ground. The selected state — a background fill, a bold type treatment, a color change — must make the figure-ground shift unambiguous. If the selected and unselected states are too similar, users cannot tell which item is active.
Ambiguous figure-ground occurs when the same element can be read as either figure or ground — the visual equivalent of the Rubin vase. In fine art and illustration this is sometimes intentional. In UI, it is almost always a failure.
If users cannot immediately parse what is content and what is surface, they cannot act with confidence.
Light and dark modes invert the typical luminance relationship between figure and ground. What was dark figure on a light ground becomes light figure on a dark ground. Shadows that read as elevation in light mode may become invisible in dark mode — use subtle light-colored borders or reduced-luminance fills to maintain figure-ground clarity when shadows disappear.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | pass→pass | 13,841 | 33,504 | +142% | 1 | 1 | 0% | 2,060 | 3,212 | +56% | 0 | 0 | — |
case-02 | fail→pass | 16,227 | 16,175 | -0% | 1 | 1 | 0% | 2,367 | 3,293 | +39% | 0 | 0 | — |
case-03 | pass→pass | 18,239 | 14,797 | -19% | 1 | 1 | 0% | 2,736 | 3,365 | +23% | 0 | 0 | — |
case-04 | pass→pass | 15,402 | 17,749 | +15% | 1 | 1 | 0% | 2,442 | 3,811 | +56% | 0 | 0 | — |
case-05 | pass→pass | 15,848 | 14,438 | -9% | 1 | 1 | 0% | 2,667 | 3,212 | +20% | 0 | 0 | — |
case-06 | pass→pass | 17,801 | 13,789 | -23% | 1 | 1 | 0% | 2,341 | 2,950 | +26% | 0 | 0 | — |
case-07 | pass→pass | 18,271 | 18,439 | +1% | 1 | 1 | 0% | 2,961 | 3,848 | +30% | 0 | 0 | — |
case-08 | pass→pass | 17,734 | 15,157 | -15% | 1 | 1 | 0% | 2,664 | 3,334 | +25% | 0 | 0 | — |
case-09 | fail→pass | 20,118 | 22,127 | +10% | 1 | 1 | 0% | 3,725 | 4,791 | +29% | 0 | 0 | — |
case-10 | pass→pass | 14,736 | 16,869 | +14% | 1 | 1 | 0% | 2,359 | 3,572 | +51% | 0 | 0 | — |
case-11 | pass→pass | 13,652 | 13,498 | -1% | 1 | 1 | 0% | 2,287 | 3,092 | +35% | 0 | 0 | — |
case-12 | pass→pass | 10,547 | 10,702 | +1% | 1 | 1 | 0% | 1,554 | 2,635 | +70% | 0 | 0 | — |
case-13 | pass→pass | 15,518 | 12,232 | -21% | 1 | 1 | 0% | 2,247 | 2,557 | +14% | 0 | 0 | — |
case-14 | pass→pass | 13,862 | 16,879 | +22% | 1 | 1 | 0% | 2,481 | 3,551 | +43% | 0 | 0 | — |
case-15 | fail→pass | 8,218 | 8,859 | +8% | 1 | 1 | 0% | 1,302 | 2,341 | +80% | 0 | 0 | — |
case-16 | fail→pass | 12,596 | 15,103 | +20% | 1 | 1 | 0% | 2,094 | 3,131 | +50% | 0 | 0 | — |
case-17 | pass→pass | 10,754 | 15,083 | +40% | 1 | 1 | 0% | 1,653 | 3,159 | +91% | 0 | 0 | — |
case-18 | fail→pass | 14,051 | 15,501 | +10% | 1 | 1 | 0% | 1,973 | 3,078 | +56% | 0 | 0 | — |
case-19 | pass→pass | 15,067 | 15,620 | +4% | 1 | 1 | 0% | 2,287 | 3,349 | +46% | 0 | 0 | — |
case-20 | fail→fail | 13,652 | 18,254 | +34% | 1 | 1 | 0% | 2,325 | 3,751 | +61% | 0 | 0 | — |
case-21 | pass→pass | 11,679 | 19,659 | +68% | 1 | 1 | 0% | 1,885 | 3,621 | +92% | 0 | 0 | — |
case-22 | pass→pass | 16,915 | 12,844 | -24% | 1 | 1 | 0% | 2,411 | 3,143 | +30% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +23 percentage points is the difference between those two pass rates over the 22 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.