by EmreKaplaner · LLM Plugin · ★ 6
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 | 6 |
| Forks | 0 |
| Language | Python |
| Category | LLM Plugin |
| License | MIT |
| Quality Score | 63/100 |
| Last Updated | 2026-09-19 |
| Created | 2026-09-18 |
| Platforms | python |
| Est. Tokens | ~17k |
Make room for useful evidence. Inspectable context selection for RAG, with Jev reranking and open benchmark studies.
Looking for a rag-jev alternative? If you're comparing rag-jev with other llm plugin tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
Ask your house a question, get a number back. Home Assistant integration for TypeSafe Jev: typed answers as se
Roy: A lightweight, model-agnostic framework for crafting advanced multi-agent systems using large language mo
MCP server for YouTube — search videos, get transcripts, channels, and playlists. Works with Claude, Cursor &
🔥🔥 Papers, open reproductions and independent evaluations behind System One models and Jev.
Automated loop driver, slash commands, council automation, MCP browser bridge, and portfolio governance for Cl
Free crypto news API - real-time aggregator for Bitcoin, Ethereum, DeFi, Solana & altcoins. No API key require
Explore other popular llm plugin 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: