No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by oxbshw · MCP Server · ★ 541
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is LLM-Agents-Ecosystem-Handbook safe to install? View the security audit →
LLM Agents & Ecosystem Handbook A unified handbook for building, deploying and understanding LLM agents and the wider ecosystem A polished, curated collection of Large Language Model (LLM) agents, tutorials and ecosystem insights. This handbook highlights projects that push
| Stars | 541 |
| Forks | 85 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 68.3382629088946/100 |
| Open Issues | 1 |
| Last Updated | 2026-06-30 |
| Created | 2025-09-08 |
| Platforms | mcp, python |
| Est. Tokens | ~17k |
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LLM-Agents-Ecosystem-Handbook is One-stop handbook for building, deploying, and understanding LLM agents with 60+ skeletons, tutorials, ecosystem guides, and evaluation tools.. It is categorized as a MCP Server with 541 GitHub stars.
LLM-Agents-Ecosystem-Handbook is primarily written in Python. It covers topics such as ai, ai-agent, ai-agents.
You can find installation instructions and usage details in the LLM-Agents-Ecosystem-Handbook GitHub repository at github.com/oxbshw/LLM-Agents-Ecosystem-Handbook. The project has 541 stars and 85 forks, indicating an active community.
LLM-Agents-Ecosystem-Handbook is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to LLM-Agents-Ecosystem-Handbook on Agent Skills Hub include sre, MakeMoneyWithAI, lucid-memory. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. 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: