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 jtlicardo · LLM Plugin · ★ 166
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is bpmn-assistant safe to install? View the security audit →
LLM-powered creation, editing, and interpretation of Business Process Model and Notation (BPMN) diagrams. https://github.com/user-attachments/assets/2f17bd99-f6fb-47a2-b33e-2190c3a834e2 Quickstart Option 1: Use the hosted version The easiest way to get started - no setup required! Visit bpmn-frontend.onrender.com and provide your own API keys directly in the interface. Your keys are stored locally in your browser and never stored on any servers. Option 2: Run locally with Docker Clone the repository Set up your environment variables Linux, macOS Windows Open the file and replace the placeholder values with your actual API keys. Build and r
| Stars | 166 |
| Forks | 45 |
| Language | Python |
| Category | LLM Plugin |
| License | MIT |
| Quality Score | 69.3966434884189/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-14 |
| Created | 2023-11-11 |
| Platforms | claude-code, gemini, python |
| Est. Tokens | ~128k |
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bpmn-assistant is LLM-powered assistant for creating, editing, and interpreting business process diagrams. It is categorized as a LLM Plugin with 166 GitHub stars.
bpmn-assistant is primarily written in Python. It covers topics such as anthropic, bpmn, claude.
You can find installation instructions and usage details in the bpmn-assistant GitHub repository at github.com/jtlicardo/bpmn-assistant. The project has 166 stars and 45 forks, indicating an active community.
bpmn-assistant is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to bpmn-assistant on Agent Skills Hub include holysheep-cli, vllora, cai. 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: