How do Claude Skills work under the hood?

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1

Hi everyone. I’m curios about the implementation of the Claude Skills. Specifically, how the system retrieves and activates the right skill at the right time.

Does anyone have insight into how that mechanism works under the hood?

2

Good afternoon, my Coach, Copilot will try to help:

:wrench: How Claude Skills Decide Which Skill to Activate

Even though Anthropic doesn’t publish every internal detail, the mechanism follows the same architectural pattern used across modern LLM tool‑calling systems. Think of it as a three‑layer loop:

:brain: 1. The Model Reads the User Message and the Skill Catalog

Claude receives:

  • The user’s message
  • A list of available Skills (each with a name, description, and input schema)

The model is trained to:

  • Recognize when a user request matches a skill’s purpose
  • Understand the schema for that skill
  • Decide whether a tool call is appropriate

This is learned behavior, not hardcoded logic.

:puzzle_piece: 2. The Model Performs “Semantic Matching”

Claude compares the user’s intent to each skill’s description.

Example:

  • User: “Summarize this PDF for me.”
  • Skill: pdf_reader → description mentions “extract text from PDFs”

The model internally evaluates:

  • Does the user’s intent match the skill’s purpose
  • Does the skill accept the right type of input
  • Is the skill required to complete the task

This is similar to how other agent frameworks choose tools based on natural‑language intent.

:gear: 3. The Model Emits a Structured Tool Call

If Claude decides a skill is needed, it outputs something like:

json

{
  "tool": "pdf_reader",
  "input": { "file_id": "123" }
}

This behavior comes from training on:

  • Function‑calling examples
  • Tool‑use demonstrations
  • Reinforcement learning where correct tool use is rewarded

The runtime then:

  • Executes the skill
  • Returns the result to Claude
  • Claude continues the conversation using that result

:compass: Why It Feels So Smart

Because the model:

  • Understands natural language deeply
  • Understands the skill descriptions
  • Has been trained on millions of examples of “when to call a tool”

It’s not rule‑based. It’s not keyword‑based. It’s semantic reasoning.

:hammer_and_wrench: A Simple Mental Model

The LLM is the brain. The skills are the hands. The brain decides which hand to use based on what each hand is designed to do.

That’s the entire mechanism.

3

@g15713, I have grown curious why every post you make on the forum is just a quote from Copilot?

That’s totally fine, but it is quite a unique form of participation within this community.

4

Thanks for asking — the reason is pretty simple. I use Copilot to help me phrase things clearly and avoid giving confusing or incomplete explanations. I’m still learning a lot of this material myself, so quoting the assistant helps me make sure I’m sharing accurate, structured information instead of guessing. This approach lets me participate without accidentally spreading misinformation.

I’m not trying to be unusual — this is just the way I contribute while I’m still building confidence with the concepts. If it ever becomes distracting, I can adjust how I present things.

I’m also building an AI medical billing support tool for small doctor offices, and that work requires a high level of accuracy. Using an AI assistant is essential for me because it helps me write clearer explanations, avoid mistakes, and stay consistent while I’m learning Python, OOP, and RAG techniques. It’s part of my workflow to make sure the information I share — both in my project and here on the forum — is structured, reliable, and easy to understand.

1 Like
5

Thanks for the background info, I found it very informative.

6

hi @g15713

do you know copilot itself mentions don’t completely rely on my information,:saluting_face::zany_face:

Also as you are working on medical billing support tool, dpo explore Practo, it is medical software for appointments, clinic look up and billing and case history! documentation tool used in India, that should help you in building your tool..

Good luck!

i always fact. check what any llm based chatbot gives response especially for medical field :slightly_smiling_face::slightly_smiling_face:

7

Thanks for the reminder — totally agree. I never rely on any AI output blindly, especially in the medical or billing domain. Everything I use from Copilot gets fact‑checked, validated, and cross‑referenced before it becomes part of my project. Accuracy and compliance matter too much in this space to take shortcuts.