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 agenticloops-ai · Agent Tool · ★ 221
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is agentic-ai-engineering safe to install? View the security audit →
English | Deutsch | Español | français | 日本語 | 한국어 | Português | 中文 Agentic AI Engineering Stop reading about agents. Start building them. This is the repo for enginee
| Stars | 221 |
| Forks | 51 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 66.0418085637617/100 |
| Open Issues | 6 |
| Last Updated | 2026-08-09 |
| Created | 2026-01-25 |
| Platforms | claude-code, python |
| Est. Tokens | ~16k |
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agentic-ai-engineering is Hands-on tutorials for building AI agents from scratch. Learn LLM APIs, prompt engineering, tool calling, and the agent loop through practical examples.. It is categorized as a Agent Tool with 221 GitHub stars.
agentic-ai-engineering is primarily written in Python. It covers topics such as agentic, agentic-ai, agentic-loop.
You can find installation instructions and usage details in the agentic-ai-engineering GitHub repository at github.com/agenticloops-ai/agentic-ai-engineering. The project has 221 stars and 51 forks, indicating an active community.
agentic-ai-engineering is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to agentic-ai-engineering on Agent Skills Hub include agentic-ai-systems, ospec, claude-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: