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 snwfdhmp · Claude Skill · ★ 922
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is awesome-ralph safe to install? View the security audit →
Awesome Ralph A curated list of resources about Ralph (aka Ralph Wiggum), the AI coding technique that runs AI coding agents in automated loops until specifications are fulfilled. Ralph is a technique created by Geoffrey Huntley for autonomous AI coding. The core concept is elegantly simple: Key principles: Run an AI agent repeatedly until a PRD/specification is complete Progress persists in files and git history, not in context Each iteration starts fresh with clean context Use "backpressure" (tests, lints, type checks) to validate work Philosophy: "Sit on the loop, not in it" — or as Huntley puts it: "deterministically bad in an undeterministic world" Found this useful? ⭐️ Like this repo to help others discover it Contents Official Resources Playbooks & Methodology Implementations Claude Code Plugins Standalone Implementations [Too
| Stars | 922 |
| Forks | 74 |
| Category | Claude Skill |
| Quality Score | 65.8172958783739/100 |
| Open Issues | 13 |
| Last Updated | 2026-02-03 |
| Created | 2026-01-19 |
| Platforms | claude-code |
| Est. Tokens | ~8k |
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awesome-ralph is A curated list of resources about Ralph, the AI coding technique that runs AI coding agents in automated loops until specifications are fulfilled.. It is categorized as a Claude Skill with 922 GitHub stars.
You can find installation instructions and usage details in the awesome-ralph GitHub repository at github.com/snwfdhmp/awesome-ralph. The project has 922 stars and 74 forks, indicating an active community.
The top alternatives to awesome-ralph on Agent Skills Hub include awesome-agent-orchestrators, crystal, agent-of-empires. 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: