by multivmlabs · Codex Skill · ★ 97
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
npm install -g ralph-starter
npx ralph-starter
Taken from the project's README on GitHub.
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.
| Stars | 97 |
| Forks | 6 |
| Language | TypeScript |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 71/100 |
| Open Issues | 19 |
| Last Updated | 2026-04-13 |
| Created | 2026-01-21 |
| Platforms | claude-code, cli, codex, node |
| Est. Tokens | ~2056k |
<a href="https://github.com
These tools work well together with ralph-starter for enhanced workflows:
Looking for a ralph-starter alternative? If you're comparing ralph-starter with other codex skill tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
Turn tracker tickets into autonomous agent sessions
Turns AI coding tools into reliable software delivery systems. Drop a .gaai/ folder into any project — Discove
Ralph Wiggum meet Kanban! Ralph style execution for [Claude Code, Codex, Pi, Cursor]. One task per iteration,
Any Agent × Any Provider × Anywhere. Connect Cursor, Claude Code, OpenCode, or Codex to Slack, Telegram, Disco
A long-running AI agent loop. Ralph automates software development tasks by iteratively working through a task
Autonomous multi-batch development skill for Claude Code and Codex. They work while you sleep.
Explore other popular codex skill tools:
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: