by day50-dev · LLM Plugin · ★ 52
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 | 52 |
| Forks | 2 |
| Language | Python |
| Category | LLM Plugin |
| License | MIT |
| Quality Score | 64/100 |
| Open Issues | 1 |
| Last Updated | 2026-11-04 |
| Created | 2026-01-09 |
| Platforms | cli, python |
| Est. Tokens | ~17k |
/usr/bin/cat for LLMs Ever need to test if an inference endpoint is working or want to one-shot a model on a server? Maybe you want to cycle through keys or models or benchmark a bank of IPs. Perhaps you want to orchestrate queries across models running on servers and want to run the job in parallel without leaving any leaky state behind.
These tools work well together with llcat for enhanced workflows:
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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: