by weam-ai · MCP Server · ★ 222
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
git clone https://github.com/weam-ai/weam.gitTaken from the project's README on GitHub.
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.
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.
| Stars | 222 |
| Forks | 92 |
| Language | TypeScript |
| Category | MCP Server |
| Quality Score | 66/100 |
| Open Issues | 23 |
| Last Updated | 2026-10-13 |
| Created | 2025-06-27 |
| Platforms | browser, claude-code, mcp, node |
| Est. Tokens | ~16k |
LLMs, Agents, AI Apps, and Team—In One Place, Ready to Use and Expand Website • Docs • Discord -- What is Weam AI Weam is an open source platform that helps teams adopt AI systematically. It includes a complete, production-ready stack with Next.js frontend and Node.js backend - ready to deploy and use immediately. System Requirements: CPU with 4+ core
Looking for a weam alternative? If you're comparing weam with other mcp server tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
[archived] c4 GenAI Suite
Flock is a workflow-based low-code platform for rapidly building chatbots, RAG, and coordinating multi-agent t
On-premises conversational RAG with configurable containers
Giselle: AI App Builder. Open Source.
An introduction to the world of AI Agents
🏠 AI-powered real estate assistant — conversational property search with RAG, 6+ LLM providers, financial too
Explore other popular mcp server tools:
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: