by innightwolfsleep · LLM Plugin · ★ 135
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 | 135 |
| Forks | 31 |
| Language | Python |
| Category | LLM Plugin |
| License | MIT |
| Quality Score | 56/100 |
| Open Issues | 12 |
| Last Updated | 2026-09-29 |
| Created | 2023-04-15 |
| Platforms | browser, python |
| Est. Tokens | ~16k |
wrAPPer for llama.cpp(default), exllama or transformers. wrAPPer for ollama (config example - config/ollamaconfig.json) wrAPPer for llmman - serves the Ollama API on port 17434 (config example - config/llmmanconfig.json) wrAPPer for llama.cpp server (config example - config/llamacppserver.json) an EXTension for oobabooga/text-generation-webui.s Provide telegram chat with various additional...
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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: