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 g2-legend-expertgit 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/g2-legend-expert)<a href="https://agentmods.dev/skills/foryourhealth111-pixel/vibe-skills/g2-legend-expert"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/g2-legend-expert/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/g2-legend-expert"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/g2-legend-expert.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 | $0.00047 | $0.01690 |
| Opus 5 | $0.00023 | $0.00845 |
| Sonnet 5 | $0.00009 | $0.00338 |
| Haiku 4.5 | $0.00005 | $0.00169 |
Grade A, and why
G2 Legend Expert 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 — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.
G2 Legend Expert Skill
Overview
This skill provides comprehensive knowledge about legend rendering in G2, covering the complete legend rendering flow from component creation to layout calculation and interaction handling.
Legend Rendering Flow in G2
The legend rendering flow in G2 follows a multi-stage process that transforms legend configuration into visual components. This flow involves several key stages:
1. Legend Component Inference
Legend components are inferred from the chart's scales and configuration during the initial setup phase. The inference process is handled by the inferComponent function in src/runtime/component.ts:
- Scale Analysis: The system analyzes all scales in the chart, looking for channels like
shape,size,color, andopacity - Legend Type Detection: Based on the scale types, the system determines whether to create
legendCategory(for discrete scales) orlegendContinuous(for continuous scales) - Position Inference: Default positions and orientations are inferred based on the chart type and coordinate system
2. Legend Component Creation
Two main legend component types are implemented:
LegendCategory
Located in src/component/legendCategory.ts, this component handles categorical legends:
- Data Processing: Processes domain values from scales to create legend items
- Marker Inference: Automatically infers appropriate markers based on the chart's shapes and scales
- Layout Wrapper: Uses
LegendCategoryLayoutto handle layout positioning - Rendering Options: Supports both standard GUI rendering and custom HTML rendering via the
renderoption
LegendContinuous
Located in src/component/legendContinuous.ts, this handles continuous legends:
- Scale Type Handling: Supports various continuous scale types including linear, log, time, quantize, quantile, and threshold
- Configuration Inference: Generates appropriate configuration based on scale properties
- Ribbon Generation: Creates color ribbons for continuous scales
- Label Formatting: Handles proper formatting of continuous scale labels
What ships with it
1 file 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.
- 9d ago First seen · 194 lines · 47 tokens per session scan A dd4d67d30884
G2 Legend Expert 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 47 tokens to every session and 1,690 once invoked, about $0.0002 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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