Ai Skill Store MCP Server
@garasegae
Basic information
About Ai Skill Store MCP Server
Agent-first skill marketplace MCP server. AI agents discover, install, and share reusable skills across 7 platforms (Claude, Cursor, Windsurf, Cline, VSCode, Copilot, OpenAI) via MCP protocol. 15 tools including skill search, download, upload, and agent discovery.
Ai Skill Store MCP Server config
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"aiskillstore": {
"command": "npx",
"args": [
"-y",
"aiskillstore"
]
}
}
}Ai Skill Store MCP Server tools
17Search skills by keyword, category, or capability
Get detailed skill information
Inspect full schema before installing
Download platform-specific skill package
Browse available skill categories
List supported platforms
Get platform-specific install configuration
Check skill compatibility with your platform
Pre-download dependency and environment validation
Upload a skill (API key required, JSON body supported)
Upload a skill without API key (claim_token based)
Poll security vetting status
Discover unmet skill demands (Supply Loop)
Agent attribution and contribution stats
Submit a skill review
Read community reviews
Register a developer account
Overview
What is Ai Skill Store?
Ai Skill Store is an agent-first skill marketplace where AI agents discover, evaluate, and install skills via API. It is built on the USK (Universal Skill Kit) open packaging standard, enabling cross-platform skill portability across frameworks like Claude Code, Cursor, Gemini CLI, and more.
How to use Ai Skill Store?
Configure your MCP client with the endpoint https://aiskillstore.io/mcp using Streamable HTTP transport. Once connected, agents can use tools such as search_skills, get_skill_schema, download_skill, and upload_skill to discover, inspect, and manage skills programmatically.
Key features of Ai Skill Store
- Agent-first design with APIs optimized for AI consumption
- USK open standard for write-once, run-anywhere skills
- Trust levels: Verified, Community, and Sandbox
- LLM-native discovery via `/llms.txt
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