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 EverMind-AI · Codex Skill · ★ 5.1k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Raven safe to install? View the security audit →
Website · 中文 Raven Raven is The Self-Improving Agent Harness, built on EverOS. Raven helps agents improve across runs by continuously refining the systems around them: tools, skills, memory, code execution, policies, and working environment. EverOS provides durable user memory, agent memory, and world knowledge across sessions,
| Stars | 5,102 |
| Forks | 145 |
| Language | Python |
| Category | Codex Skill |
| License | Apache-2.0 |
| Quality Score | 63.261513876424/100 |
| Open Issues | 98 |
| Last Updated | 2026-10-03 |
| Created | 2026-05-21 |
| Platforms | claude-code, codex, python |
| Est. Tokens | ~17k |
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Raven is The Harness of Harnesses • built for RSI: a trusted, persistent, self-evolving multi-agent ecosystem for all-domain collaboration.. It is categorized as a Codex Skill with 5.1k GitHub stars.
Raven is primarily written in Python. It covers topics such as ai, ai-agents, anthropic.
You can find installation instructions and usage details in the Raven GitHub repository at github.com/EverMind-AI/Raven. The project has 5.1k stars and 145 forks, indicating an active community.
Raven is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to Raven on Agent Skills Hub include MemOS, ai-guide, tokscale. 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: