by pablocaeg · LLM Plugin · ★ 60
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
docker run -p 8000:8000 -v sloptotal-models:/app/models ghcr.io/pablocaeg/sloptotal
git clone https://github.com/pablocaeg/sloptotal.gitTaken from the project's README on GitHub. Check the security audit before running it.
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 | 60 |
| Forks | 18 |
| Language | Python |
| Category | LLM Plugin |
| License | MIT |
| Quality Score | 69/100 |
| Open Issues | 27 |
| Last Updated | 2026-10-10 |
| Created | 2026-03-21 |
| Platforms | python |
| Est. Tokens | ~18k |
SlopTotal VirusTotal for AI-generated text. Paste text, drop in a PDF or Word file, or give it a URL. Twenty-three independent AI detectors (neural classifiers, statistical tests and linguistic heuristics) score it in parallel, and a calibrated ensemble turns their votes into one verdict you can inspect engine by engine. It runs on your o
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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: