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 Q00 · MCP Server · ★ 6.1k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is ouroboros safe to install? View the security audit →
English | 한국어 ◯ ─────────── ◯ O U R O B O R O S ◯ ─────────── ◯ Stop prompting. Start specifying. Agent OS for replayable, specification-first AI coding workflows Quick Start · Why · Results · How It Works · Commands · Philosophy Turn a vague idea into a verified, working codebase -- across Claude Code, Codex CLI, OpenCode, and Hermes. Ouroboros is an Agent OS for AI coding: a local-first runtime layer that turns non-deterministic agent work into a replayable, observable, policy-bound execution contract. It replaces ad-hoc prom
| Stars | 6,055 |
| Forks | 609 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 64.9790483497412/100 |
| Open Issues | 104 |
| Last Updated | 2026-09-21 |
| Created | 2026-01-14 |
| Platforms | claude-code, cli, codex, gemini, mcp, python |
| Est. Tokens | ~18k |
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ouroboros is Agent OS: the agent gets smarter on its own. We just hold the line: Interview-gated, staged evaluation, budgeted evolution loop. MCP server, 14 runtimes: Claude Code, Codex CLI, Gemini CLI, OpenCode, . It is categorized as a MCP Server with 6.1k GitHub stars.
ouroboros is primarily written in Python. It covers topics such as agent-os, agentic-ai, ai-agent.
You can find installation instructions and usage details in the ouroboros GitHub repository at github.com/Q00/ouroboros. The project has 6.1k stars and 609 forks, indicating an active community.
ouroboros is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to ouroboros on Agent Skills Hub include memsearch, openpencil, context-mode. 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: