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 Keesan12 · MCP Server · ★ 190
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is martin-loop safe to install? View the security audit →
MartinLoop MartinLoop gives AI coding agents budgets, stop conditions, rollback rules, and receipts. Built from thousands of agent runs where the problem was not intelligence -- it was uncontrolled execution. Get started: Try the demo: MartinLoop is part of the NVIDIA Inception program. Why MartinLoop AI coding agents are useful, but unbounded retry loops are e
| Stars | 190 |
| Forks | 48 |
| Language | TypeScript |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 70.1163118018472/100 |
| Last Updated | 2026-09-19 |
| Created | 2026-04-04 |
| Platforms | claude-code, codex, mcp, node |
| Est. Tokens | ~18k |
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martin-loop is Make AI coding agents safe to scale autonomously: assign work, cap spend, enforce policy, verify output, roll back failures, learn from loops, and prove ROI across every repo.. It is categorized as a MCP Server with 190 GitHub stars.
martin-loop is primarily written in TypeScript. It covers topics such as agent-runtime, ai-agent-runtime, ai-coding-agents.
You can find installation instructions and usage details in the martin-loop GitHub repository at github.com/Keesan12/martin-loop. The project has 190 stars and 48 forks, indicating an active community.
martin-loop is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to martin-loop on Agent Skills Hub include cordum, template-repo, deterministic-agent-control-protocol. 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: