by PageAI-Pro · Codex Skill · ★ 322
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
npx @pageai/ralph-loopTaken 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 | 322 |
| Forks | 45 |
| Language | Shell |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 73/100 |
| Open Issues | 3 |
| Last Updated | 2026-08-31 |
| Created | 2026-01-26 |
| Platforms | claude-code, cli, codex |
| Est. Tokens | ~15k |
A Ralph Wiggum Loop implementation that works™ 📚 Full docs: ralphloop.sh Ralph is a long-running AI agent loop. Ralph automates software development tasks by iteratively working through a task list until completion. This allows for long running agent loops, effectively enabling AI to code for days at a time.
These tools work well together with ralph-loop for enhanced workflows:
Looking for a ralph-loop alternative? If you're comparing ralph-loop 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.
Extended Ralph loop for autonomous AI-driven plan execution
Lightweight Agent Workstation for Codex + Claude code + keke-agent — with dots, task scheduler, git worktree &
A curated list of resources about Ralph, the AI coding technique that runs AI coding agents in automated loops
A better /goal
Spec-driven development with smart compaction. Claude Code plugin combining Ralph Wiggum loop with structured
Bootstrap projects from Figma, Linear, Notion & GitHub specs. The only AI ralph with source integrations.
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: