Vibe-Skills is a collection and routing system that helps AI agents discover, select, and coordinate specialized skills for completing tasks. It is intended for agents that need to organize workflows across many installed capabilities. The catalogue entries are skills and an agent belonging to this system.
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 foryourhealth111-pixel/Vibe-Skills --skill data-artistgit clone --depth 1 https://github.com/foryourhealth111-pixel/Vibe-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/foryourhealth111-pixel/vibe-skills/data-artist)<a href="https://agentmods.dev/skills/foryourhealth111-pixel/vibe-skills/data-artist"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/data-artist/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/foryourhealth111-pixel/vibe-skills/data-artist"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/data-artist.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00028 | $0.01840 |
| Opus 5 | $0.00014 | $0.00920 |
| Sonnet 5 | $0.00006 | $0.00368 |
| Haiku 4.5 | $0.00003 | $0.00184 |
Grade A, and why
data-artist 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 12d 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 — 279 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Artist
You are creating a work of data art. This skill brings together mathematical elegance, emotional resonance, narrative design, and technical excellence to transform raw data into something beautiful that tells a story and moves the viewer.
The "Data is Beautiful" Philosophy
Core Principles
- Life is Beautiful - Data visualization should reveal the wonder in information
- Mathematical Elegance - Perceptually accurate encodings, thoughtful scales
- Emotional Resonance - Create moments of awe, reflection, insight
- Swiss Minimalism - Clean geometry, purposeful color, no chartjunk
- Narrative Journey - Guide the viewer through a story
What Makes Data Beautiful
- Clarity - The data speaks clearly without distortion
- Proportion - Visual weight matches data importance
- Rhythm - Patterns emerge naturally from the encoding
- Surprise - Reveals insights not obvious in raw numbers
- Humanity - Connects data to human experience
Visualization Domains
1. Mathematical Foundations (@geepers_datavis_math)
Scale Selection:
- Linear for comparison
- Log for orders of magnitude
- Sqrt for area perception
- Time scales for temporal data
Visual Encoding:
- Position (most accurate)
- Length/height (good)
- Angle/slope (moderate)
- Area (requires sqrt scaling)
- Color intensity (least precise)
Perceptual Accuracy:
- Ensure encodings don't mislead
- Account for human perception biases
- Use perceptually uniform color scales
2. Color Design (@geepers_datavis_color)
Palette Types:
- Sequential: Low → High (single hue)
- Diverging: Negative ↔ Neutral ↔ Positive
- Categorical: Distinct groups (max 7-9)
Color Principles:
- Perceptual uniformity (Lab/HCL color space)
- Colorblind accessibility (avoid red-green only)
- Emotional resonance (warm/cool, muted/vibrant)
- Cultural considerations
Signature Palettes:
/* Elegant Sequential */
--seq-1: #F7FBFF;
--seq-2: #DEEBF7;
--seq-3: #9ECAE1;
--seq-4: #4292C6;
--seq-5: #084594;
/* Thoughtful Diverging */
--div-neg: #B2182B;
--div-neutral: #F7F7F7;
--div-pos: #2166AC;
/* Accessible Categorical */
--cat-1: #1B9E77;
--cat-2: #D95F02;
--cat-3: #7570B3;
--cat-4: #E7298A;
--cat-5: #66A61E;
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.
- 12d ago First seen · 279 lines · 28 tokens per session scan A 59c500e9e121
data-artist is a skill published in the GitHub repository foryourhealth111-pixel/Vibe-Skills (3,252 stars, last pushed 12d ago), licensed Apache-2.0. It adds 28 tokens to every session and 1,840 once invoked, about $0.0001 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 2026-08-30.
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