No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by imxv · Codex Skill · ★ 1.1k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Pretty-mermaid-skills safe to install? View the security audit →
Pretty Mermaid Beautiful Mermaid diagrams for AI agents Turn Mermaid source into polished SVGs, shareable PNGs, and terminal-ready ASCII—locally, without a browser. English ｜ 中文 ｜ 日本語 🚀 Install View the skill, install count, and security audits on skills.sh → Why Pretty Mermaid? Made for AI agents: works with Claude Code, Cursor, Codex, Gemini CLI, and more One source, three outputs: polished SVG for docs, PNG for sharing, and ASCII/Unicode for terminals No browser required: renders locally without Chromium, Puppetee
| Stars | 1,138 |
| Forks | 57 |
| Language | JavaScript |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 69.5548084104075/100 |
| Last Updated | 2026-08-22 |
| Created | 2026-01-30 |
| Platforms | browser, claude-code, cli, codex, node |
| Est. Tokens | ~14k |
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Pretty-mermaid-skills is AI Agent Skill to generate and render beautiful Mermaid diagrams as SVG or terminal ASCII — 15 themes, 6 diagram types, batch CLI, no browser.. It is categorized as a Codex Skill with 1.1k GitHub stars.
Pretty-mermaid-skills is primarily written in JavaScript. It covers topics such as agent-skills, ai-agents, ascii-art.
You can find installation instructions and usage details in the Pretty-mermaid-skills GitHub repository at github.com/imxv/Pretty-mermaid-skills. The project has 1.1k stars and 57 forks, indicating an active community.
Pretty-mermaid-skills is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to Pretty-mermaid-skills on Agent Skills Hub include claude-workflow-v2, Generative-Media-Skills, claude-code-plugins-plus-skills. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: