by Das-rebel · LLM Plugin · ★ 16
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 | 16 |
| Forks | 5 |
| Language | TypeScript |
| Category | LLM Plugin |
| License | MIT |
| Quality Score | 64/100 |
| Open Issues | 3 |
| Last Updated | 2026-10-11 |
| Created | 2026-05-15 |
| Platforms | cli, node |
| Est. Tokens | ~15k |
⚡ Adaptive multi-model LLM router — 80+ providers, Jev System One single-pass routing (model=jev-auto), pheromone-trail failover, parallel ensemble merge. npm: adaptive-memory-multi-model-router
Looking for a a3m-router alternative? If you're comparing a3m-router 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.
One key. Every model. Anywhere. — A curated map of AI API relay stations & LLM gateways.
LLM orchestration toolkit for agent workflows: planner + workers + synthesis, optional router (LLM + learned
✋ Interactive CLI tool for selecting and bundling code into a single, LLM-ready output file
Governance gateway for AI agents — bounded, auditable, session-aware control with MCP proxy, shell proxy & HTT
Pure Front-End LLM API Testing Tool
🏛 [UNDER CONSTRUCTION] A (roman) claude plugin marketplace
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: