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 kyegomez · Claude Skill · ★ 14.9k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is OpenMythos safe to install? View the security audit →
OpenMythos Disclaimer: OpenMythos is an independent, community-driven theoretical reconstruction based solely on publicly available research and speculation. It is not affiliated with, endorsed by, or connected to Anthropic or any of their proprietary systems. OpenMythos is an open-source, theoretical implementation of the Claude Mythos model. It implements a Recurrent-Depth Transformer (RDT) with three stages: Prelude (transformer blocks), a looped Recurrent Block (up to ), and a final Coda. Attention is switchable between MLA and GQA, and the feed-forward uses a sparse MoE with routed and shared experts ideal for exploring compute-adaptive, depth-variable reasoning.
| Stars | 14,902 |
| Forks | 3,296 |
| Language | Python |
| Category | Claude Skill |
| License | MIT |
| Quality Score | 69.4536052006991/100 |
| Open Issues | 61 |
| Last Updated | 2026-05-23 |
| Created | 2026-04-18 |
| Platforms | claude-code, python |
| Est. Tokens | ~8k |
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OpenMythos is A theoretical reconstruction of the Claude Mythos architecture, built from first principles using the available research literature.. It is categorized as a Claude Skill with 14.9k GitHub stars.
OpenMythos is primarily written in Python. It covers topics such as ai, anthropic, attention.
You can find installation instructions and usage details in the OpenMythos GitHub repository at github.com/kyegomez/OpenMythos. The project has 14.9k stars and 3296 forks, indicating an active community.
OpenMythos is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to OpenMythos on Agent Skills Hub include zcf, claude-code-guide, LibreChat. 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: