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 svd-ai-lab · Codex Skill · ★ 216
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is sim-cli safe to install? View the security audit →
sim-cli lets AI agents operate CAE solvers one verified step at a time. is an open-source CLI and local runtime that lets Codex, Claude Code, GitHub Copilot, Gemini, and other agents work with simulation software through solver-specific plugins and bundled skills. An agent can check what is installed, connect to a solver, inspect live state, execute bounded modeling or analysis steps, verify result/state, and save checkpoints/artifacts for engineering review. <img src="https://img.shields.io/badge/status-alpha-f97316"
| Stars | 216 |
| Forks | 22 |
| Language | Python |
| Category | Codex Skill |
| License | Apache-2.0 |
| Quality Score | 66.9261031109751/100 |
| Last Updated | 2026-09-08 |
| Created | 2026-04-07 |
| Platforms | claude-code, cli, codex, python |
| Est. Tokens | ~16k |
These tools work well together with sim-cli for enhanced workflows:
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sim-cli is Turn existing CAE files into structured text an agent can use — COMSOL, Abaqus, Fluent, HFSS, Icepak, FloTHERM — plus solver detection, script linting, and live solver sessions. Strong coverage for el. It is categorized as a Codex Skill with 216 GitHub stars.
sim-cli is primarily written in Python. It covers topics such as abaqus, advanced-packaging, agent-skills.
You can find installation instructions and usage details in the sim-cli GitHub repository at github.com/svd-ai-lab/sim-cli. The project has 216 stars and 22 forks, indicating an active community.
sim-cli is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to sim-cli on Agent Skills Hub include agent-designer, pydantic-deepagents, codexia. 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: