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 duolahypercho · Codex Skill · ★ 3.5k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is codex-router safe to install? View the security audit →
Codex Router Use Kimi, DeepSeek, and future external models from the normal Codex model picker without replacing native GPT models or your ChatGPT sign-in. Codex Router is an independent community project. It is not affiliated with OpenAI, Moonshot AI, DeepSeek, OpenRouter, or the referenced opencodex project. Give the link to Codex Paste this into a Codex task: If a compatible credential already exists, Codex can complete everything except the final app restart. API keys are entered only in a hidden terminal prompt. Or run the guided installer macOS or Linux: Windows PowerShell: The setup asks which providers to show, detects existing authentication, offers to run
| Stars | 3,465 |
| Forks | 275 |
| Language | JavaScript |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 69.9462246743391/100 |
| Open Issues | 18 |
| Last Updated | 2026-09-14 |
| Created | 2026-07-19 |
| Platforms | codex, node |
| Est. Tokens | ~15k |
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codex-router is External-model router for Codex with guided Kimi OAuth/API, DeepSeek, safe migration, and rollback.. It is categorized as a Codex Skill with 3.5k GitHub stars.
codex-router is primarily written in JavaScript. It covers topics such as codex, deepseek, kimi.
You can find installation instructions and usage details in the codex-router GitHub repository at github.com/duolahypercho/codex-router. The project has 3.5k stars and 275 forks, indicating an active community.
codex-router is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to codex-router on Agent Skills Hub include u-claw, codecompanion.nvim, ouroboros. 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: