LLM-Agents-Ecosystem-Handbook: One-stop handbook for building, deploying, and understanding LLM agents with 60+ skeletons, tutorials

by oxbshw · MCP Server · ★ 555

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 68/100

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.

aiai-agentai-agentsfine-tuningfinetuning-llmsfreameworkllmllmopslocal-developmentmcp-server

Quick Facts

Stars555
Forks93
LanguagePython
CategoryMCP Server
LicenseMIT
Quality Score68/100
Open Issues4
Last Updated2026-06-30
Created2025-09-08
Platformsmcp, python
Est. Tokens~17k

About LLM-Agents-Ecosystem-Handbook

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

Compatible Skills

These tools work well together with LLM-Agents-Ecosystem-Handbook for enhanced workflows:

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  • MetaClaw — semantic(0.18)+complementary+rare_topics+same_lang+similar_pop+shared_platform (56%)
  • awesome-azure-openai-llm — semantic(0.17)+complementary+rare_topics+same_lang+similar_pop+shared_platform (56%)

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How this security grade is produced

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:

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