by snwfdhmp · Claude Skill · ★ 922
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
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. Full audit →
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 | 922 |
| Forks | 74 |
| Category | Claude Skill |
| Quality Score | 66/100 |
| Open Issues | 13 |
| Last Updated | 2026-02-03 |
| Created | 2026-01-19 |
| Platforms | claude-code |
| Est. Tokens | ~8k |
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.
These tools work well together with awesome-ralph for enhanced workflows:
Looking for a awesome-ralph alternative? If you're comparing awesome-ralph with other claude skill tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
List of agent orchestrators
(Crystal is now Nimbalyst) Run multiple Codex and Claude Code AI sessions in parallel git worktrees. Test, com
Spec-driven development with smart compaction. Claude Code plugin combining Ralph Wiggum loop with structured
A CLI tool for logging and analyzing Claude Code and Cursor ai-driven coding session.
A long-running AI agent loop. Ralph automates software development tasks by iteratively working through a task
Code search MCP for Claude Code. Make entire codebase the context for any coding agent. Embeddings are created
Explore other popular claude 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: