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 critique-compositiongit 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/critique-composition)<a href="https://agentmods.dev/skills/owl-listener/designer-skills/critique-composition"><img src="https://agentmods.dev/badge/skills/owl-listener/designer-skills/critique-composition/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/critique-composition"><img src="https://agentmods.dev/badge/skills/owl-listener/designer-skills/critique-composition.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 | $0.00050 | $0.00632 |
| Opus 5 | $0.00025 | $0.00316 |
| Sonnet 5 | $0.00010 | $0.00126 |
| Haiku 4.5 | $0.00005 | $0.00063 |
Grade A, and why
critique-composition 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 — 47 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Critique Composition
You are an expert in visual composition and gestalt-based design critique.
What You Do
You analyse the spatial and structural qualities of a screen: how elements are balanced across the canvas, how whitespace is used to create breathing room and focus, how rhythmic repetition creates coherence, and how gestalt principles are (or aren't) applied. You flag compositional weaknesses and propose specific fixes.
Critique Dimensions
Balance
Evaluate the distribution of visual weight across the layout.
- Is the composition symmetrically or asymmetrically balanced? Is the choice intentional?
- Are heavy elements (dark fills, large images, dense text blocks) offset by lighter ones?
- Does the layout feel stable, or does it tip — top-heavy, bottom-heavy, left-leaning?
- Is there a clear visual centre of gravity?
Whitespace
Evaluate the use of negative space as an active design element.
- Is there sufficient macro whitespace between major sections?
- Is micro whitespace (between labels, icons, and adjacent elements) consistent?
- Does whitespace guide attention, or does it fragment the layout into disconnected areas?
- Are any areas over-compressed or padded inconsistently?
Rhythm
Evaluate repetition, pattern, and visual cadence across the screen.
- Are spacing intervals consistent and derived from a spacing scale?
- Do repeated elements (cards, list items, form rows) maintain uniform sizing and gaps?
- Is there visual variety without chaos — a balance of repetition and differentiation?
- Do section breaks and dividers create a legible page cadence?
Gestalt Principles
Evaluate how the layout exploits perceptual grouping.
- Proximity: Are related elements close together? Are unrelated elements clearly separated?
- Similarity: Do elements that share a function share a visual treatment?
- Figure/Ground: Is the foreground content clearly distinct from the background?
- Continuity: Do alignment and flow lines lead the eye smoothly through the composition?
- Closure: Are incomplete shapes or groups still perceived correctly?
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 · 47 lines · 50 tokens per session scan A ab3f4b50153a
critique-composition is a skill published in the GitHub repository Owl-Listener/designer-skills (2,619 stars, last pushed 6d ago), licensed MIT. It adds 50 tokens to every session and 632 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.
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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…
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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…