skill-mcp
Related Servers
Alternatives to skill-mcp
No user-submitted related servers found.
Related Servers
- AlicenseNot gradedqualityBmaintenanceServes any directory of Agent Skills (SKILL.md folders) over MCP, exposing skills/list, skills/get, and skill:// resources so clients can enumerate and retrieve each skill's frontmatter and files. Every returned file comes with a real SHA-256 digest, letting clients verify content rather than merely trust it.3 npmMIT
- AlicenseNot gradedqualityCmaintenanceDynamically loads and exposes skills from a directory with hot-reloading support, enabling users to manage and query skill content via MCP resources and tools.13MIT
- AlicenseAqualityCmaintenanceConverts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.344 PyPI29Apache 2.0
- AlicenseNot gradedqualityCmaintenanceTurns any folder of Markdown agent skills into a local MCP server, exposing them as tools, prompts, and resources for MCP-compatible clients.MIT

craftbag-mcpofficial
AlicenseNot gradedqualityAmaintenanceEnables MCP hosts to discover, list, load, explain, and validate Agent Skills (SKILL.md) from project and home skill trees. Hosts can filter, rank, and load skills without depending on any one agent product.2MIT- AlicenseNot gradedqualityCmaintenanceServes Agent Skills over MCP using the SEP-2640 Skills Extension, enabling skills discovery, retrieval, and resource reading via MCP.Apache 2.0
TDQS
Scored across 4 tools
Each tool targets a distinct stage of the skill lifecycle: listing skills, loading a SKILL.md, reading bundled files, and executing declared scripts. There is no overlap—file reading and script execution are clearly separated, and descriptions reinforce the boundaries.
All four tools use a uniform skill_<verb> pattern (skill_list, skill_load, skill_file, skill_run). The convention is predictable and readable throughout.
Four tools is well-scoped for a skill-discovery and execution server. Each tool earns its place with no redundant or filler operations.
The surface covers the full lifecycle: enumerate skills, load instructions, read bundled files, and run declared scripts, with clear guidance on chaining (e.g. read files after load). No obvious gaps or dead ends for the stated purpose.