by alfredolopez80 · Claude Skill · ★ 146
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🔒 Is multi-agent-ralph-loop safe to install? View the security audit →
Multi-Agent Ralph Loop Autonomous orchestration framework for Claude Code with MemPalace-inspired memory, parallel-first Agent Teams, Aristotle First Principles methodology, and quality gates. What It Does Ralph extends Claude Code into a multi-agent development framework with a structured memory system inspired by the Memory Palace technique. Every task is analyzed from first principles, decomposed into parallel subtasks, assigned to specialized teammates, and validated through quality gates before completion. MemPalace Memory System Inspired by the MemPalace repository (Memory Palace technique f
git clone https://github.com/alfredolopez80/multi-agent-ralph-loop.gitTaken from the project's README on GitHub. Check the security audit before running it.
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 →
| Stars | 146 |
| Forks | 21 |
| Language | Shell |
| Category | Claude Skill |
| Quality Score | 70.8883620456639/100 |
| Last Updated | 2026-09-03 |
| Created | 2026-01-02 |
| Platforms | claude-code, cli, codex |
| Est. Tokens | ~13k |
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multi-agent-ralph-loop is Autonomous orchestration framework for Claude Code with MemPalace-inspired memory (4-layer stack, 818-token wake-up), parallel-first Agent Teams (6 teammates), Aristotle First Principles methodology, . It is categorized as a Claude Skill with 146 GitHub stars.
multi-agent-ralph-loop is primarily written in Shell. It covers topics such as ai-orchestration, automation, bats-testing.
The README on GitHub (github.com/alfredolopez80/multi-agent-ralph-loop) gives: git clone https://github.com/alfredolopez80/multi-agent-ralph-loop.git. Usage details are in the same README.
The top alternatives to multi-agent-ralph-loop on Agent Skills Hub include vnx-orchestration, bug-hunter, the-pair. 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: