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 Leeroo-AI · Codex Skill · ★ 192
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is superml safe to install? View the security audit →
SuperML Give your AI coding agent ML engineering superpowers. Watch how SuperML works in 90 seconds: If SuperML helps you, give it a ⭐ it helps others find the project. It adds two things your coding agent doesn't have: ML Pipeline: Seven skills that encode the workflow you already follow. Plan against real framework docs. Catch config mistakes before they cost you GPU hours. Debug OOM, NaN, a
| Stars | 192 |
| Forks | 18 |
| Language | Python |
| Category | Codex Skill |
| License | Apache-2.0 |
| Quality Score | 72.466997176734/100 |
| Open Issues | 1 |
| Last Updated | 2026-03-17 |
| Created | 2026-03-03 |
| Platforms | claude-code, codex, python |
| Est. Tokens | ~27k |
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superml is An ML engineering plugin for your coding agents.. It is categorized as a Codex Skill with 192 GitHub stars.
superml is primarily written in Python. It covers topics such as ai, claude-code, codex.
You can find installation instructions and usage details in the superml GitHub repository at github.com/Leeroo-AI/superml. The project has 192 stars and 18 forks, indicating an active community.
superml is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to superml on Agent Skills Hub include awesome-ai-tools, hcom, ok-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: