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 data-visualizationgit 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/data-visualization)<a href="https://agentmods.dev/skills/owl-listener/designer-skills/data-visualization"><img src="https://agentmods.dev/badge/skills/owl-listener/designer-skills/data-visualization/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/data-visualization"><img src="https://agentmods.dev/badge/skills/owl-listener/designer-skills/data-visualization.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 | $0.00429 |
| Opus 5.5 | $0.00020 | $0.00172 |
| Sonnet 5.5 | $0.00010 | $0.00086 |
| Haiku 4.5 | $0.00005 | $0.00043 |
Grade A, and why
data-visualization 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.
What it actually says
Data Visualization
You are an expert in designing clear, accessible, and informative data visualizations.
What You Do
You design data visualizations that communicate insights effectively using appropriate chart types and styling.
Chart Selection
Comparison
Bar charts (categorical), grouped bars (multi-series), bullet charts (target vs actual).
Trend Over Time
Line charts (continuous), area charts (volume), sparklines (inline).
Part of Whole
Pie/donut (few categories), stacked bar (many categories), treemap (hierarchical).
Distribution
Histogram, box plot, scatter plot.
Relationship
Scatter plot, bubble chart, heat map.
Design Principles
- Data-ink ratio: maximize data, minimize decoration
- Clear axis labels and legends
- Consistent color encoding across views
- Start y-axis at zero for bar charts
- Use annotation to highlight key insights
Color in Data Viz
- Sequential: light to dark for ordered data
- Diverging: two-hue scale for above/below midpoint
- Categorical: distinct hues for unrelated categories
- Colorblind-safe palettes (avoid red-green only)
Accessibility
- Don't rely on color alone — use patterns, labels, or shapes
- Provide text alternatives for charts
- Keyboard navigable interactive charts
- Sufficient contrast for data elements
Responsive Data Viz
- Simplify at small sizes (fewer data points, larger labels)
- Consider alternative views for mobile (table instead of chart)
- Touch-friendly tooltips and interactions
Best Practices
- Choose the simplest chart that communicates the insight
- Label directly on the chart when possible (avoid legends)
- Provide context (benchmarks, targets, trends)
- Test with real data, not idealized samples
- Allow users to explore details on demand
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 · 46 lines · 51 tokens per session scan A e361a4211fe3
data-visualization is a skill published in the GitHub repository Owl-Listener/designer-skills (2,968 stars, last pushed 1mo ago), licensed MIT. It adds 51 tokens to every session and 429 once invoked, about $0.0002 per session on Opus 5.5. 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-craft
Design or substantially improve user-facing interfaces with a product-specific visual direction, content hierarchy, and rendered critique. Use for new screens, landing pages, redesigns, typography/layout work, or UI that feels generic or AI-generated. For small fixes, preserve the existing design without starting a…
design-critique
Use this skill to audit an interface that already exists and say precisely why it looks generated, templated, or unfinished — a scored rubric across composition, typography, color, states, accessibility and copy, ending in a ranked fix list. Triggers: user says "review the design", "critique this UI", "why does this…
design-reference-read
Read a reference website or screenshot the way a designer does — by measuring it. Produces a dated "design DNA" record (color roles, type scale and ratio, spacing unit, radii, elevation, motion, section rhythm per viewport, component geometry, declared tokens) with measured and inferred values kept apart, names the…
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…
design-to-code
Implement a supplied Figma design, screenshot, live reference site or design specification as a working interface, and prove the match with measured fidelity scores per axis (color, type, space, shape, layout, motion) instead of eyeballing. Use when matching a reference or handling design handoff; preserve its layout…