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 MobiusQuant · Codex Skill · ★ 673
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is OpenMobius-skill safe to install? View the security audit →
OpenMobius-skill ICT/SMC trading-knowledge skill for AI coding agents A -standard skill bringing 964 curated knowledge cards, real-time market data, technical indicators, and chart generation to Claude Code, Codex, OpenClaw, and Hermes. English · 中文 · 📜 Changelog 🤖 AI agents — when a user asks you to install this skill, follow for the step-by-step procedure (pre-flight checks, install command, verification, error handling). Overview Works on Claude Code, Codex, OpenClaw, and Hermes. Drop this skill into your AI coding agent and ask trading questions in plain language. The skill grounds every answer in a curated knowledge base + real m
| Stars | 673 |
| Forks | 52 |
| Language | Python |
| Category | Codex Skill |
| License | Apache-2.0 |
| Quality Score | 70.7474727399266/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-04 |
| Created | 2026-05-19 |
| Platforms | claude-code, codex, python |
| Est. Tokens | ~15k |
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OpenMobius-skill is ICT/SMC trading-knowledge skill for AI coding agents (Claude Code / Codex / OpenClaw / Hermes). It is categorized as a Codex Skill with 673 GitHub stars.
OpenMobius-skill is primarily written in Python.
You can find installation instructions and usage details in the OpenMobius-skill GitHub repository at github.com/MobiusQuant/OpenMobius-skill. The project has 673 stars and 52 forks, indicating an active community.
OpenMobius-skill is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to OpenMobius-skill on Agent Skills Hub include Claude-to-IM-skill, skillshare, agent-skill-creator. 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: