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 beizhu-1209 · MCP Server · ★ 1.0k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is AIHelms safe to install? View the security audit →
企业 AI 资源纳管平台 承载 AI 数字资产 · 释放 AI 生产力 · 链接未来 核心价值 · 功能展示 · 快速开始 · 部署文档 这是什么 AIHelms 是面向企业的 AI 资源纳管平台。帮助企业管理 AI 资产、控制 AI 成本、衡量 AI 价值的管理工具。 决策层 - 平台全程记录用量、核算成本、生成报表，让看得见 AI 的投入产出比 管理员 -
| Stars | 1,033 |
| Forks | 104 |
| Language | Python |
| Category | MCP Server |
| License | GPL-3.0 |
| Quality Score | 64.6260014140958/100 |
| Open Issues | 2 |
| Last Updated | 2026-09-10 |
| Created | 2026-05-09 |
| Platforms | claude-code, docker, mcp, python |
| Est. Tokens | ~14k |
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AIHelms is 企业级 AI 资源纳管平台，提供统一 AI网关、Token调度能力，纳管 OpenAI、Azure、Claude、DeepSeek 等主流模型，并支持 MCP 工具与 Skill 的集中注册分发。具备内外双轨定价、成本归因、统一身份认证、安全审计与效能报表，帮助企业精准控制成本、量化 ROI，高效治理 AI 资产。. It is categorized as a MCP Server with 1.0k GitHub stars.
AIHelms is primarily written in Python. It covers topics such as agent, ai-gateway, ai-management.
You can find installation instructions and usage details in the AIHelms GitHub repository at github.com/beizhu-1209/AIHelms. The project has 1.0k stars and 104 forks, indicating an active community.
AIHelms is released under the GPL-3.0 license, making it free to use and modify according to the license terms.
The top alternatives to AIHelms on Agent Skills Hub include archestra, oreilly-ai-agents, casibase. 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: