by emicklei · MCP Server · ★ 214
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
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 | 214 |
| Forks | 18 |
| Language | Go |
| Category | MCP Server |
| License | MIT |
| Quality Score | 63/100 |
| Last Updated | 2026-10-23 |
| Created | 2014-05-03 |
| Platforms | go, mcp |
| Est. Tokens | ~15k |
Melrōse - programming of music melodies Introduction is a tool to create and play music by programming melodies. It uses a custom language to compose notes and create loops and tracks to play. This is an example of a simple major scale C. Note sequences in your program can be changed while playing giving you direct audible feedback.
These tools work well together with melrose for enhanced workflows:
Looking for a melrose alternative? If you're comparing melrose 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.
Simple, modular, and observable Go framework for backend applications.
Ultimate Context Engineering Infrastructure, starting from MCPs and Integrations
The AI control plane for every agent in your enterprise, built around an enterprise-grade MCP gateway. Give ea
A lightweight Model Context Protocol (MCP) server that enables AI assistants like Claude to retrieve and inter
MCP Server for Bear note taking app available as Claude Desktop extension or standalone server for any other A
A Model Context Protocol (MCP) server that provides programmatic control over MuseScore!
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: