by emirsahin1 · Agent Tool · ★ 276
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
pip install llm-axeTaken 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 | 276 |
| Forks | 38 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 73/100 |
| Last Updated | 2025-01-05 |
| Created | 2024-04-22 |
| Platforms | python |
| Est. Tokens | ~17k |
llm-axe Goal llm-axe is meant to be a flexible toolkit that provides simple abstractions for commonly used functions related to LLMs. It's not meant to intrude in your development workflow as other larger frameworks often do. It has functions for automatic schema generation, pre-made agents with self-tracking chat history and fully customizable agents.
Looking for a llm-axe alternative? If you're comparing llm-axe 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.
The coding agent that gets more out of your AI subscription. Fastest and leanest of 20 agents tested, with unl
The AI Operating System for Delphi. 100% native framework with RAG 2.0, autonomous agents, MCP protocol, and u
GPT-4 level function calling models for real-world tool using use cases
An Automated AI-Powered Prompt Optimization Framework
User friendly CLI tool for AI tasks. Stop thinking about LLMs and prompts, start getting results!
Command a fleet of AI agents, on every deck, every desk, every wrist. Self-hosted multi-agent platform with me
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: