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 marchatton/agent-skills --skill beautiful-mermaidgit clone --depth 1 https://github.com/marchatton/agent-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/marchatton/agent-skills/beautiful-mermaid)<a href="https://agentmods.dev/skills/marchatton/agent-skills/beautiful-mermaid"><img src="https://agentmods.dev/badge/skills/marchatton/agent-skills/beautiful-mermaid/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/marchatton/agent-skills/beautiful-mermaid"><img src="https://agentmods.dev/badge/skills/marchatton/agent-skills/beautiful-mermaid.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00037 | $0.00538 |
| Opus 5 | $0.00018 | $0.00269 |
| Sonnet 5 | $0.00007 | $0.00108 |
| Haiku 4.5 | $0.00004 | $0.00054 |
Grade A, and why
beautiful-mermaid 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 11d 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
Beautiful Mermaid
Purpose
Mermaid code -> SVG or ASCII/Unicode. Themed output. TS-first.
When
- Need Mermaid render without DOM.
- Need themed SVG for web/assets.
- Need terminal-safe ASCII/Unicode.
Install
pnpm add beautiful-mermaid
API
renderMermaid(code, opts?) -> Promise<string>(SVG)renderMermaidAscii(code, opts?) -> Promise<string>(ASCII/Unicode)fromShikiTheme(themeName) -> MermaidConfig["theme"]
Example (TS)
import { renderMermaid, renderMermaidAscii, fromShikiTheme } from "beautiful-mermaid";
const code = "flowchart LR\nA-->B";
const svg = await renderMermaid(code, {
theme: fromShikiTheme("catppuccin-mocha"),
backgroundColor: "#1e1e2e",
});
const ascii = await renderMermaidAscii(code, {
useUnicode: true,
maxWidth: 80,
});
renderMermaid opts
theme: MermaidConfig["theme"]. Default"default".backgroundColor: string. Default"transparent".themeConfig: MermaidConfig["themeConfig"].mermaidConfig: MermaidConfig.svgOptimize: SvgoConfig orfalse.
renderMermaidAscii opts
useUnicode: boolean. Defaulttrue.wrap: boolean. Defaulttrue.maxWidth: number. Default80.bg: string. Default"#FFFFFF".fg: string. Default"#000000".
Built-in themes
default, forest, dark, neutral, base, ocean, dimmed, darkes, light, catppuccin-latte, catppuccin-frappe, catppuccin-macchiato, catppuccin-mocha, dracula, dracula-soft, one-dark-pro, material-dark, material-light, material-darker, material-palenight, material-ocean, material-lighter, tokyo-night, tokyo-night-storm, tokyo-night-light, github-light, github-dark, github-dark-dimmed
Output
- SVG string: save
.svgor inline in HTML. - ASCII/Unicode string: print to terminal/log.
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.
- 11d ago First seen · 61 lines · 37 tokens per session scan A 9b6014e9ae3e
beautiful-mermaid is a skill published in the GitHub repository marchatton/agent-skills (5 stars, last pushed 6mo ago), licensed MIT. It adds 37 tokens to every session and 538 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 2026-08-31.
Other skills, from other repositories
webgl-holographic-foil
A self-contained WebGL2 hero: thin-film interference over a crushed-foil surface whose palette shifts with the viewing angle; move the cursor to tilt the film.
general-video
Author or edit a custom HyperFrames composition when no specialized workflow fits, or when BRIEF.md sets flow: companion. Use for longer or multi-scene pieces, brand and sizzle reels, montages, static loops, static title cards, footage remixes, and freeform builds. Use motion-graphics instead for a short unnarrated…
html-ppt-hermes-cyber-terminal
OpenDesign + BYOK: choosing and wiring your own model, hands-on — cost, quality, and the routing decision. Built as a decision-grade AI literacy deck for engineers, IT, applied-AI teams.
html-ppt-taste-brutalist
16:9 HTML deck in tactical-telemetry / CRT-terminal taste. Deactivated-CRT charcoal slides, white-phosphor monospace, hazard-red accent, scanline overlay, ASCII syntax, density over decoration. Distilled from Leonxlnx/taste-skill brutalist-skill (Tactical Telemetry mode).
chengfeng-check-updates
An environment manager for a video-editing system. It checks whether its skills and runtime—the software needed to run them—are installed and compatible.
diagnostic-stem-delivery
Audio production with diagnostic analysis, timecode parsing from documents, and verified export workflow.