The Four Components Behind Every AI Tool

Most people treat AI like a magic box, but once you understand the four components that power Claude Skills, you'll recognise the same architecture across ChatGPT, Notion agents, and virtually every AI tool you use.

A Claude Skill is a pre-built capability designed for specific tasks. Pull it apart and you find four components that appear in nearly every AI system.

System instruction

This is the job description. It tells the AI who it is, what it does, how to do it, and what to avoid. In Claude Skills, this is the core prompt that defines behaviour, tone, scope and guardrails.

The same concept appears everywhere. Custom GPTs have system instructions. Notion agents have them. Claude Code projects have them. The concept stays identical - only the name and location change.

Router

The router is decision-making logic. It reads your request and determines which path to take - which instructions to prioritise, which context to pull in, how to structure the response.

Once you know AI tools route requests through logic paths, you stop treating prompts like lottery tickets. You start thinking about which path you want the AI to take and how to guide it there.

Context files

These are the reference materials the AI can pull from. In Claude Skills, they're documents, examples and background information. In Custom GPTs, they're uploaded knowledge files. In Notion agents, they're accessible databases and pages. In Claude Code, they're project files and documentation.

Same concept, different packaging.

Naming and organisation

How you name and organise files, instructions and context affects how the AI processes information. Claude Skills use progressive loading - the system loads what it needs when it needs it, rather than dumping everything into memory at once.

If you're paying for a ChatGPT or Claude subscription, you won't feel the impact of token optimisation. But when you build agents or use Claude Code with your own API key, every token costs money. Clean naming and smart organisation save actual pounds.

Even without touching API keys, well-organised system instructions consistently produce better outputs than messy ones.

Why this matters

These four components aren't unique to Claude Skills. They're the underlying architecture of virtually every AI tool. The vocabulary changes, the interface changes, but the structure remains constant.

Understanding this architecture changes how you work. Your prompts become more precise because you know what the AI needs. Building agents shifts from trial and error to following a blueprint. Token optimisation becomes a practical decision rather than an abstract concept.

The architecture also creates a natural learning path. You see it clearly in Claude Skills, apply it in Notion agents using databases as your context layer, then take full control in Claude Code. Each step builds on the previous one using the same underlying structure.