We are working in a small project where we are implementing the solution using Claude. From the work being done, skills emerged as a great way to automate certain things that are maturing and turning repetitive tasks.
So, the next natural thing that happened is that we start to produce a good list of skills, each one in its own computer. We share with the others, but, the next relevant question was:
how do we transition from monolithic collections of AI skills toward a more structured architecture?
The discussion revealed that the answer is not “move everything to MCP” or “keep everything as skills.”
The answer is more nuanced.
When you have small amount of skills this works beautifully:
- Skills are easy to create,
- Skills are portable,
- Teams can share them,
- Developers can iterate rapidly
But this growth creates new challenges:
- Large numbers of skills consume context
- Discovery becomes difficult
- Execution costs increase
- Governance becomes harder
- Multiple teams begin creating overlapping capabilities
Eventually the team reach a point where simply adding more skills becomes unsustainable.
How do we organize all these micro-capabilities?
One of the strongest conclusions from the discussion was that skills should be classified into three distinct categories.
Category 1: Deterministic Workloads → MCP Tools
These are predictable operations with fixed inputs and outputs.
Characteristics:
- Deterministic behavior
- Little or no judgment required
- Compute-heavy execution
- Stable business logic
- Well-defined contracts
Examples:
- Data transformation,
- testing,
- document parsing,
- financial calculations
Instead of having an AI reason through every execution step, the operation is exposed as an MCP tool. The AI invokes the tool, receives a strongly typed result, and continues working.
Category 2: Judgment-Driven Workflows → Skills
Some tasks require the AI to think.
These workflows:
- Adapt dynamically
- Make decisions during execution
- Change path based on observations
- Require contextual reasoning
Examples:
- Requirements analysis
- Architecture reviews
- Complex document generation
In these situations, a skill remains the best abstraction. Here it’s worthy to pay for the tokens. The value comes from the model’s reasoning, not from deterministic execution.
Moving these workflows into rigid tools typically reduces flexibility and increases maintenance burden.
These workflows remain AI-native.
Category 3: Continuously Changing Logic → Event-Driven Automation
A third category emerged that neither tools nor skills handle particularly well.
This includes logic that:
- Changes hourly
- Evolves with every commit
- Requires continuous validation
- Depends on rapidly changing rules
A good example is scalability review or code governance.
In these situations:
- Maintaining a skill becomes expensive
- Converting to a formal tool becomes impractical
The better solution is often an automated loop triggered directly by events.
Examples:
- Pull request validation
- Security scanning
- Policy enforcement
- Continuous architecture review
- Compliance checks
Instead of being manually invoked, these systems execute automatically when relevant events occur.
The architecture becomes:
Plain Text → Event→ Automation Loop → Analysis → Feedback
No human invocation required.
Takeaways
There are many implications apart of these three choices, but you need to start with something and this simple decision making mechanisms help a lot the team to make decisions.
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