What is a Skill?

Fri, 31 Jul 2026 00:00:00 +0000

A skill is a Markdown file an agent loads on demand to learn how to handle a particular kind of request. It’s useful when you have a repetitive task and don’t want to re-prompt your agent each time.

Think of it as the utility function of prompting: instead of duplicating the same instructions in every conversation, you write them once and reuse them. Skills are more general than that, of course — their behaviour adapts to the request in a way a single utility function doesn’t.

What is MCP (Model Context Protocol)?

Mon, 20 Jul 2026 00:00:00 +0000

This section briefly explains what MCP is and why it’s useful. Before describing MCP, it helps to understand tool-calling first — MCP is built on top of it.

Tool-calling

Tool-calling is a capability that lets an AI model (like an LLM) interact with the outside world by invoking external functions or APIs.

Here’s a simple example of tool-calling in Python with two tools, bash_tool and web_search:

# mock tools
def bash_tool(command: str) -> str:
    """Run a shell command and return its output."""
    return "index.html  main.py  styles.css  README.md" if command == "ls" else "Done"


def web_search(query: str) -> str:
    """Search the web and return the results."""
    return f"Search results for '{query}': Found documentation."


tools = {"bash_tool": bash_tool, "web_search": web_search}

# Simulates the AI picking a tool based on keywords
def mock_llm(query):
    if "file" in query or "list" in query:
        return {"tool": "bash_tool", "arguments": {"command": "ls"}}
    return {"tool": "web_search", "arguments": {"query": query}}


while True:
    user_query = input("User: > ")
    if user_query.lower() in ["exit", "quit"]:
        print("Goodbye!")
        break

    # Step 1: Get tool choice from LLM
    decision = mock_llm(user_query)
    tool_name, args = decision["tool"], decision["arguments"]
    print(f"AI wants to call: {tool_name}({args})")

    # Step 2 & 3: execute the tool and print the output
    tool_output = tools[tool_name](**args)
    print(f"Tool Output: {tool_output}\n")

That’s tool-calling in a nutshell: the model picks a tool (e.g. web_search or bash_tool) and supplies the right arguments (e.g. query: "who won the 2022 World Cup"), the tool or API is executed — locally or remotely — and the model reads back the result.

Building Blocks on Amir Hadifar

What is a Skill?

What is MCP (Model Context Protocol)?

Tool-calling