by caura-ai · Codex Skill · ★ 83
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.
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 | 83 |
| Forks | 5 |
| Language | Python |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 62/100 |
| Open Issues | 27 |
| Last Updated | 2026-10-22 |
| Created | 2026-05-28 |
| Platforms | python |
| Est. Tokens | ~20k |
<a href="https://nodejs.or
These tools work well together with caura-long-run-fleet for enhanced workflows:
Looking for a caura-long-run-fleet alternative? If you're comparing caura-long-run-fleet 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.
Local-first memory governance for AI agents: shared, reviewable, auditable memory via SQLite and MCP.
Local-first persistent agentic memory powered by Recursive Memory Harness (RMH). Open source must win.
Command a fleet of AI agents, on every deck, every desk, every wrist. Self-hosted multi-agent platform with me
Memory for AI that works like yours—local, instant, persistent. 13x faster than Pinecone, 5x leaner than RAG.
🚀 JoySafeter: An enterprise AI Agent Platform—Not just chatting. building、running、testing, and tracing autono
Reusable project context and specialist workflows for the AI tools you already use. Agents, skills, rules and
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: