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 datavisgit 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/datavis)<a href="https://agentmods.dev/skills/foryourhealth111-pixel/vibe-skills/datavis"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/datavis/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/datavis"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/datavis.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 | $0.00106 | $0.01180 |
| Opus 5 | $0.00053 | $0.00590 |
| Sonnet 5 | $0.00021 | $0.00236 |
| Haiku 4.5 | $0.00011 | $0.00118 |
Grade A, and why
datavis 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 13d 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 — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Visualization Skill
Create beautiful, mathematically elegant, emotionally resonant data visualizations.
Philosophy: "Life is Beautiful"
Every visualization should:
- Reveal truth through data
- Evoke wonder through design
- Respect the viewer through accessibility
- Honor complexity through elegant simplification
Core Capabilities
1. Visual Encoding
Scale Selection:
| Scale | Use When | Example |
|---|---|---|
| Linear | Evenly distributed data | Temperature |
| Log | Multiple orders of magnitude | Population (100 to 1B) |
| Sqrt | Encoding area (circles) | Bubble chart radius |
| Time | Temporal data | Dates |
Perceptual Honesty - Area scales with square of radius, so use sqrt:
// WRONG: Linear radius exaggerates large values
const badScale = d3.scaleLinear().domain([0, max]).range([0, maxRadius]);
// RIGHT: Sqrt maintains perceptual accuracy
const goodScale = d3.scaleSqrt().domain([0, max]).range([0, maxRadius]);
2. Color Design
Palette Types:
- Categorical - Distinct hues for nominal data (max 8)
- Sequential - Single hue gradient for ordered data
- Diverging - Two hues meeting at meaningful midpoint
Colorblind-Safe Palette (8 colors):
const colorblindSafe = [
'#332288', '#117733', '#44AA99', '#88CCEE',
'#DDCC77', '#CC6677', '#AA4499', '#882255'
];
Always use redundant encoding - don't rely on color alone:
node.attr('fill', d => colorScale(d.category))
.attr('d', d => symbolScale(d.category)); // Shape too!
3. D3.js Patterns
Force Simulation:
const simulation = d3.forceSimulation(nodes)
.force('charge', d3.forceManyBody().strength(-300))
.force('link', d3.forceLink(links).id(d => d.id))
.force('center', d3.forceCenter(width/2, height/2))
.force('collision', d3.forceCollide().radius(d => d.r + 2));
Responsive SVG:
const svg = d3.select('#chart')
.append('svg')
.attr('viewBox', `0 0 ${width} ${height}`)
.attr('preserveAspectRatio', 'xMidYMid meet');
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 13d ago First seen · 154 lines · 106 tokens per session scan A d6f3a480f289
datavis 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 106 tokens to every session and 1,180 once invoked, about $0.0005 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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api-baas-neon
Serverless PostgreSQL with branching, autoscaling, and edge-compatible driver.
api-database-postgresql
Direct PostgreSQL access with node-postgres (pg) -- connection pools, parameterized queries, transactions, streaming, LISTEN/NOTIFY, error handling.