aixyz — security grade SAFE, quality 66/100

Security audit verdict: SAFE · quality 66/100

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 AgentlyHQ · MCP Server · ★ 82

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

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About aixyz

Nextjs-like framework for bundling AI agents into deployable services with A2A, MCP, x402 payments, and ERC-8004 identity. Documentation · Quick Start · How It Works · Examples · CLI · Protocols <img src="https://img.shields.io/npm/l/aixyz?colorA=f6f8fa&colorB=f6f8

aiai-agentsmcppaymentx402

Quick Facts

Stars82
Forks9
LanguageTypeScript
CategoryMCP Server
Quality Score66.2009958384484/100
Open Issues39
Last Updated2026-07-17
Created2026-01-13
Platformsmcp, node
Est. Tokens~384k

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Frequently Asked Questions

What is aixyz?

aixyz is Next.js-like framework building AI Agents that are payment-native. Bootstrap your agent with A2A, MCP, x402 payments, and ERC-8004 identity.. It is categorized as a MCP Server with 82 GitHub stars.

What programming language is aixyz written in?

aixyz is primarily written in TypeScript. It covers topics such as ai, ai-agents, mcp.

How do I install or use aixyz?

You can find installation instructions and usage details in the aixyz GitHub repository at github.com/AgentlyHQ/aixyz. The project has 82 stars and 9 forks, indicating an active community.

What are the best alternatives to aixyz?

The top alternatives to aixyz on Agent Skills Hub include Context-Engine, daymon, deepcontext-mcp. 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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