Claude Code: How Subagents, Tools, and Skills Fit Together

· 2 min read ·
claude-code ai-tools subagents skills mcp

Last time I wrote about Claude Code plugins, the focus was on a system of sub-agents — specialists who each have tools (including MCP) at their disposal.

Soon after, Anthropic announced the addition of “skills.”

Since then, a few people have asked me how sub-agents, skills, and tools differ — and it’s easy to get them mixed up. Here’s my simple mental model to keep it clear.

What Anthropic Says

The docs are technically comprehensive, but I like to remember it in a more intuitive, human way.

Level 1: The Team

At the highest level, talking to Claude is like talking to one team of experts. Their overall purpose is described in the claude.md file — think of it as your “team charter.”

Level 2: The Sub-Agents

The second level is the specialists on that team — the sub-agents.

Depending on your context, they can be:

  • Proactively invoked by you, or
  • Automatically triggered when Claude detects a relevant keyword.

Each sub-agent has its own job description, defined by you (just like you define claude.md for the overall team).

For example: You might define a sub-agent as a “Best Practice Researcher” — someone great at scanning internal/external documentation, summarizing insights, and sharing them back.

Level 3: Skills vs. Tools (Where Most People Get Confused)

This is where things blur — so I find it easiest to think about it like describing a real person:

  • Skills — These are abilities like writing, design, or research. They’re not tied to any specific app or platform.

  • Tools — These are applications that apply those skills — like using Figma to design, or Google Docs to write. Most tools connect through MCP (Model Context Protocol) that you set up.

Example: Adding Skills to Sub-Agents

I’ve added some skills to each sub-agent in my repo: https://github.com/casper7995/claude-code-architect-copilot

Examples:

  • Databricks-PySpark skills — For Delta Lake, DLT, and MLflow pipelines

  • Python-ML-DataScience skills — For feature engineering, model development, and experimentation

Closing Thoughts

If you think of Claude as a team:

  • Sub-agents are your specialists

  • Skills are their abilities

  • Tools are the apps they use to do the work

That framing has helped me explain it clearly to others — and keeps my Claude setup easier to reason about as it scales.