AI Tools

    Skills vs Projects vs Subagents vs MCP: One Decision Tree

    James KillickJames Killick21 Jun 20268 min read
    claude skillsclaude projectssubagentsmcpai orchestration

    TL;DR

    • Projects give Claude knowledge. Skills give Claude a method. Subagents split the work. MCP connects Claude to your tools.
    • Most confusion comes from treating these as rivals. They are not. They do different jobs and stack together.
    • Quick rule: knowledge to know (Projects), how to do it (Skills), help to do it (Subagents), tools to act on (MCP).
    • Pick by the question you are stuck on, not by which one sounds most powerful.
    • Here is the one decision tree that ends the guessing.

    People keep asking me whether they should use a Skill, a Project, a subagent, or set up MCP. The question itself shows the confusion. These are not four options for the same job. They are four different jobs.

    I run Devwiz and the Njin Method across 14+ clients, and once a team gets this map, the choices stop being hard. Here is the simple version.


    What does each one actually do?

    Projects hold knowledge. Skills hold method. Subagents add hands. MCP adds reach. Four jobs, not four rivals.

    Strip the jargon and each has one job.

    • Projects are about knowledge. Load your files and context so Claude understands your world. The what.
    • Skills are about method. A saved procedure so Claude does a task your way, every time. The how.
    • Subagents are about hands. Helpers that take a chunk of work, do it in their own space, and report back. The who.
    • MCP is about reach. The connection that lets Claude touch your tools, data, and apps, built on an open standard Anthropic introduced. The where it acts.

    Knowledge, method, hands, reach. Hold those four words and the rest falls into place.


    The one decision tree

    Ask what you are missing. Need Claude to know your stuff, use a Project. Need it to do a task the same way, use a Skill. Need more done at once, use Subagents. Need it to act in your tools, use MCP.

    Start with the question you are actually stuck on.

    "Claude does not understand my business or my files."
    You need knowledge. Set up a Claude Project and load your context.

    "Claude does the task differently every time."
    You need method. Build a Claude Skill that locks in how the job is done.

    "The job is too big and the work is getting messy."
    You need hands. Split it with subagents so each part runs clean in its own space.

    "Claude can talk about it but cannot actually do it in my tools."
    You need reach. Connect it through MCP, the open standard for plugging AI into tools, so it can act on your systems.

    Most real setups use several at once. A Project for context, Skills for the methods, subagents for the heavy jobs, MCP for the tools. They stack. They do not compete.


    Why people pick the wrong one

    The usual mistake is reaching for the most powerful-sounding option for every job. The skill is matching the tool to the gap, not always grabbing the biggest hammer.

    I tell a story about model choice that fits here perfectly. A mate kept reaching for the most powerful model for every tiny task and kept hitting his limits. I told him it is like using a Ferrari for the school pickup run. Brilliant car, wrong job.

    Same trap with these four. People hear "MCP" or "subagents," decide that is the advanced move, and reach for it when all they needed was a Project with better files. Power for the sake of power just burns time and adds complexity.

    The fix is to know your lane. Match the tool to the gap in front of you. I am the same in my own work. I spend my energy where I am strong and get help with the parts that drain me. Pick the piece that solves your actual problem, not the one that sounds most impressive.


    How they stack in a real setup

    A complete setup uses all four. Knowledge in a Project, method in Skills, scale through subagents, action through MCP. That stack is what turns a chatbot into a system.

    Here is what a real build looks like when it is doing serious work.

    You load the business context into a Project, so Claude understands the world. You add Skills for the tasks you repeat, so they run the same way every time. When a job is big, the main agent hands parts to subagents, so nothing gets muddy. And MCP connects the whole thing to your real tools, so it can actually do the work, not just describe it.

    That is the move from "AI that chats" to "AI that runs a process." It is the backbone of agentic AI for business, and it is exactly the system we build with teams at The Orchestrators.


    Quick comparison

    Piece Its job Reach for it when
    Projects Knowledge Claude needs to understand your files and world
    Skills Method The task must be done the same way every time
    Subagents Hands The job is too big or messy for one agent
    MCP Reach Claude needs to act inside your real tools

    If you are still choosing between the broader Claude tools first, start with Claude Cowork vs Claude Code. For how MCP compares to older integration methods, see MCP vs API.


    FAQ

    What is the difference between Skills and Projects?
    Projects give Claude knowledge to draw on. Skills give Claude a method to follow. Knowledge is the what, method is the how. See Claude Skills explained.

    When should I use subagents instead of one agent?
    When a job is too big or messy for one agent to handle cleanly. Subagents split the work so each part runs in its own space and reports back tidy.

    What is MCP in simple terms?
    MCP is the connection that lets Claude act inside your tools, data, and apps. It is reach. See what is an MCP server.

    Do I need all four?
    Not to start. Begin with the one that solves your current problem. Serious setups end up using all four together, because they do different jobs.

    Which is the most powerful?
    Wrong question. The most useful one is whichever fills the gap you are stuck on. Power you do not need just adds complexity.


    The bottom line

    Projects are knowledge. Skills are method. Subagents are hands. MCP is reach. Four jobs, not four rivals.

    Pick by the question you are stuck on, not by which sounds most advanced. Match the tool to the gap, and the whole thing gets simple.

    Want help designing the right stack for your business instead of guessing? Book an AI transformation session and we will map knowledge, method, hands, and reach to your actual goals.

    About the Author

    James Killick
    James Killick

    The AI Orchestrator

    AI Orchestrator and entrepreneur with 10+ years building digital products. Helping $1M+ business owners scale with AI systems, automation, and implementation through The AI Orchestrators, Devwiz, and Njin.

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