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 Optim-Agent · Codex Skill · ★ 1.0k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is optim-agent safe to install? View the security audit →
optim-agent Agentic system optimization with coding agents. Automate the iterative parameter-tuning work of an algorithm engineer. English | 简体中文 | 日本語 | 한국어 | Français | Deutsch | Español | Português | Русский Documentation optim-agent uses Claude Code, Codex, or OpenCode to
| Stars | 1,008 |
| Forks | 42 |
| Language | Python |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 67.3675051217302/100 |
| Last Updated | 2026-08-14 |
| Created | 2026-07-07 |
| Platforms | claude-code, cli, codex, python |
| Est. Tokens | ~18k |
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optim-agent is LLM agents as your hyperparameter optimizer.. It is categorized as a Codex Skill with 1.0k GitHub stars.
optim-agent is primarily written in Python. It covers topics such as agent-skills, ai-agents, automl.
You can find installation instructions and usage details in the optim-agent GitHub repository at github.com/Optim-Agent/optim-agent. The project has 1.0k stars and 42 forks, indicating an active community.
optim-agent is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to optim-agent on Agent Skills Hub include ai-devkit, ResearcherSkill, 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: