by stloendays · MCP Server · ★ 191
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
git clone https://github.com/stloendays/Superpower.git
Taken 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 | 191 |
| Forks | 18 |
| Language | TypeScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 67/100 |
| Open Issues | 13 |
| Last Updated | 2026-09-28 |
| Created | 2026-08-24 |
| Platforms | browser, mcp, node |
| Est. Tokens | ~17k |
Superpower One MCP layer for browser AI and native desktop workflows. English · 简体中文 Problem · Products · Tour ·
These tools work well together with Superpower for enhanced workflows:
Looking for a Superpower alternative? If you're comparing Superpower 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.
MCP server for SSH: let Claude Code, Codex and any MCP client run commands, transfer files and manage servers
AI-powered annotations for localhost development. Create visual feedback on your apps and let AI coding agents
The open-source AI agent control plane and software factory: govern every tool and model call.
A modern desktop application for managing Model Context Protocol (MCP) servers across multiple AI agents
AgentChat 是一个基于 LLM 的智能体交流平台,内置默认 Agent 并支持用户自定义 Agent。通过多轮对话和任务协作,Agent 可以理解并协助完成复杂任务。项目集成 LangChain、Function
Production-Ready MCP Server Framework • Build, deploy & scale secure AI agent infrastructure • Includes Auth,
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: