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 shanraisshan · Codex Skill · ★ 957
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is codex-cli-best-practice safe to install? View the security audit →
codex-cli-best-practice practice makes codex perfect -white?style=flat&labelColor=555) = Agents · = Commands · = Skills 🧠 CONCEPTS Built-in slash commands for session control — examples include , , ,
| Stars | 957 |
| Forks | 64 |
| Language | Python |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 59.7759023040286/100 |
| Last Updated | 2026-06-04 |
| Created | 2026-02-27 |
| Platforms | cli, codex, python |
| Est. Tokens | ~402k |
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codex-cli-best-practice is from vibe coding to agentic engineering - practice makes codex perfect. It is categorized as a Codex Skill with 957 GitHub stars.
codex-cli-best-practice is primarily written in Python. It covers topics such as agentic-ai, agentic-coding, agentic-engineering.
You can find installation instructions and usage details in the codex-cli-best-practice GitHub repository at github.com/shanraisshan/codex-cli-best-practice. The project has 957 stars and 64 forks, indicating an active community.
codex-cli-best-practice is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to codex-cli-best-practice on Agent Skills Hub include Mysti, codexia, AI-Agents-Projects-Tutorials. 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: