by arinbjornk · Agent Tool · ★ 68
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
pip install llmtopTaken from the project's README on GitHub.
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 | 68 |
| Forks | 9 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 74/100 |
| Open Issues | 3 |
| Last Updated | 2025-12-23 |
| Created | 2024-12-23 |
| Platforms | python |
| Est. Tokens | ~93k |
llmtop is an intelligent system monitoring tool that combines real-time system metrics with LLM-powered insights. It provides a dynamic terminal interface showing system performance metrics enhanced with AI-driven analysis. Note: This project is currently in beta testing. Features and interfaces may change.
Looking for a llmtop alternative? If you're comparing llmtop with other agent tool tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
Ultra-fast token & cost tracker for LLM Token Usage (e.g. Claude Code)
Open-source terminal AI coding-agent harness for studying agent loops, tools, MCP, skills, safety, and persist
This MCP server provides documentation about Strands Agents to your GenAI tools, so you can use your favorite
A CLI tool to provide LLM context for coding projects by combining project files into a single text file (or c
[archived] c4 GenAI Suite
Dialectical reasoning architecture for LLMs (Thesis → Antithesis → Synthesis)
Explore other popular agent tool 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: