๊ฐ€์ž… ํ›„ ์ดˆ๋Œ€ ๋งํฌ๋ฅผ ๊ณต์œ ํ•˜๋ฉด ๋™์˜์ƒ ์žฌ์ƒ ๋ฐ ์ดˆ๋Œ€ ๋ณด์ƒ์„ ๋ฐ›์„ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.

Akshay ๐Ÿš€
@akshay_pachaar
Simplifying LLMs, AI Agents, RAG, and Machine Learning for you! โ€ข Co-founder @dailydoseofds_โ€ข BITS Pilani โ€ข 3 Patents โ€ข ex-AI Engineer @ LightningAI
๊ฐ€์ž… July 2012
501 ํŒ”๋กœ์ž‰ ์ค‘    290K ํŒฌ
MCP meets agent skills MCP already gave agents a standard way to connect to tools, resources, and external systems. Now it also defines a standard way to discover and load Agent Skills directly from MCP servers. The flow is simple: โ†’ connect to MCP server โ†’ discover available skills โ†’ inspect skill metadata โ†’ load the relevant ๐—ฆ๐—ž๐—œ๐—Ÿ๐—Ÿ.๐—บ๐—ฑ only when needed Under the hood, Skills are served through MCPโ€™s existing Resources primitive. That means ๐—ฆ๐—ž๐—œ๐—Ÿ๐—Ÿ.๐—บ๐—ฑ, references, scripts, examples, and other supporting files are exposed as resources that the client can read on demand. This is especially useful for context window management. Instead of loading every workflow instruction upfront, the agent can first discover what skills are available and pull in only the one required for the current task. A useful mental model is: tools = what the agent can do resources = what the agent can access skills = how the agent should perform a reusable workflow Previously, that workflow knowledge often lived separately in docs, repos, prompt files, or custom integrations. Now the MCP server can expose the capability and the playbook for using it together. So you get: โ†’ standardized skill discovery โ†’ on-demand context loading โ†’ cleaner distribution and versioning โ†’ reusable workflows that travel with the server MCP was already the connection layer. Skills now add a standardized way to ship reusable agent know-how on top of it. The illustration below visually summarizes everything that we discussed so far. Read more: Cheers! :)
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