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 cobusgreyling · MCP Server · ★ 11.4k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is loop-engineering safe to install? View the security audit →
Loop Engineering <a href="https://cobusgreyling.github.io/loop-eng
| Stars | 11,406 |
| Forks | 1,531 |
| Language | TypeScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 71.6185723954711/100 |
| Open Issues | 17 |
| Last Updated | 2026-10-03 |
| Created | 2026-06-09 |
| Platforms | claude-code, cli, codex, mcp, node |
| Est. Tokens | ~16k |
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loop-engineering is Practical patterns, starters & CLI tools for loop engineering with AI coding agents. Design systems that prompt and orchestrate agents (inspired by Addy Osmani and Boris Cherny). Includes loop-audit, . It is categorized as a MCP Server with 11.4k GitHub stars.
loop-engineering is primarily written in TypeScript. It covers topics such as agentic-ai, ai-agents, ai-coding.
You can find installation instructions and usage details in the loop-engineering GitHub repository at github.com/cobusgreyling/loop-engineering. The project has 11.4k stars and 1531 forks, indicating an active community.
loop-engineering is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to loop-engineering on Agent Skills Hub include agents, cursor-talk-to-figma-mcp, superset. 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: