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 fcakyon · Claude Skill · ★ 1.2k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is claude-codex-settings safe to install? View the security audit →
My daily battle-tested Claude Code/Desktop and OpenAI Codex setup with skills, commands, hooks, subagents and MCP servers. Installation • Plugins • Configuration • Statusline • References Installation Prerequisites: Before installing, ensure you have Claude Code and required tools installed. See INSTALL.md for complete prerequisites. Install agents, commands, hooks, skills, and MCP servers via Claude Code Plugins system: bash Add marketplace /plugin marketplace add fcakyon/claude-codex-settings Install plugins (pick what you need) /plugin install a
| Stars | 1,152 |
| Forks | 110 |
| Language | Python |
| Category | Claude Skill |
| License | Apache-2.0 |
| Quality Score | 68.0826821160666/100 |
| Open Issues | 6 |
| Last Updated | 2026-09-23 |
| Created | 2025-07-09 |
| Platforms | claude-code, cli, codex, gemini, python |
| Est. Tokens | ~18k |
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claude-codex-settings is Battle-tested Claude Code, OpenAI Codex, Cursor configs, plugins, hooks and agents with Kimi, MiniMax and GLM API support.. It is categorized as a Claude Skill with 1.2k GitHub stars.
claude-codex-settings is primarily written in Python. It covers topics such as ai-agents, ai-tools, claude-ai.
You can find installation instructions and usage details in the claude-codex-settings GitHub repository at github.com/fcakyon/claude-codex-settings. The project has 1.2k stars and 110 forks, indicating an active community.
claude-codex-settings is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to claude-codex-settings on Agent Skills Hub include pro-workflow, quint-code, 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: