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BeBraveBeKind/mcpskills-server

by Various

Pre-install trust layer for MCP servers, AI skills & npm packages — the @mcpskillsio/server MCP server (io.mcpskills/server).

B

MCP

BeBraveBeKind/mcpskills-server

Added 11 June 2026

#ai #claude #mcp #mcp-server #model-context-protocol #security #trust-scoring

Overview

A pre-install trust layer for MCP servers, AI skills, and npm packages. It is implemented as the @mcpskillsio/server MCP server (io.mcpskills/server) and runs in JavaScript environments. The tool aims to verify the trustworthiness of components before they are installed.

Best for

Best for
Developers who need a pre-install trust layer for MCP servers and related components

Use cases

  • Verifying trust of MCP servers before deployment
  • Screening AI skill packages for security issues
  • Adding a trust check step to npm package installations

Notes

A pre-install trust layer for MCP servers, AI skills, and npm packages. It is implemented as the @mcpskillsio/server MCP server (io.mcpskills/server) and runs in JavaScript environments. The tool aims to verify the trustworthiness of components before they are installed.

0 stars on GitHub. Last updated 2026-06-09. Licensed MIT.

Use cases

  • Verifying trust of MCP servers before deployment
  • Screening AI skill packages for security issues
  • Adding a trust check step to npm package installations

Pros

  • Unified trust verification for three different software types
  • Integrates directly with the MCP ecosystem
  • Open source and publicly available on GitHub

Cons

  • Zero stars indicates very limited community adoption so far
  • Requires integration and may not work out-of-box with all workflows
  • Trust mechanism depends on the specific implementation and may need vetting

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Unified trust verification for three different software types
  • Integrates directly with the MCP ecosystem
  • Open source and publicly available on GitHub

Cons

  • Zero stars indicates very limited community adoption so far
  • Requires integration and may not work out-of-box with all workflows
  • Trust mechanism depends on the specific implementation and may need vetting