codex-cli-nix: Nix flake for OpenAI Codex CLI - native Rust binary, hourly updates, multi-platform caching

by sadjow · Codex Skill · ★ 168

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install codex-cli-nix

git clone https://github.com/sadjow/codex-cli-nix

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 77/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

automationcachixclicodexdevelopment-toolsdevtoolsnixnix-flakenixosnodejs

Quick Facts

Stars168
Forks27
LanguageShell
CategoryCodex Skill
LicenseMIT
Quality Score77/100
Last Updated2026-11-06
Created2025-09-04
Platformscli, codex
Est. Tokens~12k

About codex-cli-nix

codex-cli-nix Always up-to-date Nix package for OpenAI Codex - lightweight AI coding agent in your terminal. 🚀 Automatically updated hourly to ensure you always have the latest Codex version. 🦀 Now using native Rust binaries - no Node.js dependency required! Why this package? Primary Goal: Always Up-to-Date Codex for Nix Users This flake provides immediate access to the latest OpenAI Codex...

Compatible Skills

These tools work well together with codex-cli-nix for enhanced workflows:

  • nix-config — semantic(0.53)+complementary+rare_topics+similar_pop (58%)
  • rigup.nix — semantic(0.51)+complementary+rare_topics+similar_pop (57%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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:

View on GitHub → Browse Codex Skill tools