ai-microcore — security grade SAFE, quality 71/100

Security audit verdict: SAFE · quality 71/100

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 Nayjest · MCP Server · ★ 108

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

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About ai-microcore

AI MicroCore: A Minimalistic Foundation for AI Applications MicroCore is a

aianthropicgenerative-aigenerative-ai-toolsgptllmllm-frameworkmcpnlpopenai

Quick Facts

Stars108
Forks7
LanguagePython
CategoryMCP Server
LicenseMIT
Quality Score71.2165190987099/100
Open Issues31
Last Updated2026-08-25
Created2023-06-29
Platformsmcp, python
Est. Tokens~17k

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Frequently Asked Questions

What is ai-microcore?

ai-microcore is A handy lib for smooth interaction with large language models (LLMs) and crafting AI apps.. It is categorized as a MCP Server with 108 GitHub stars.

What programming language is ai-microcore written in?

ai-microcore is primarily written in Python. It covers topics such as ai, anthropic, generative-ai.

How do I install or use ai-microcore?

You can find installation instructions and usage details in the ai-microcore GitHub repository at github.com/Nayjest/ai-microcore. The project has 108 stars and 7 forks, indicating an active community.

What license does ai-microcore use?

ai-microcore is released under the MIT license, making it free to use and modify according to the license terms.

What are the best alternatives to ai-microcore?

The top alternatives to ai-microcore on Agent Skills Hub include repopack-py, context-compressor, palico-ai. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

How this security grade is produced

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:

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