Automating Repetitive Work with a Skill + MCP Server: a practical pattern

The useful part

That system uses a Python pipeline with explicit curator and reviewer components: structured prompts, JSON outputs, and a sequence of LLM calls that runs without a human watching. We've built a few internal tools around the same pattern: a repetitive workflow, data sitting in one or more external systems, and a human who needs to stay in the loop at the right moments. Each time we reach for a solution, we face the same question: how much infrastructure does this actually need?

How it works

• When this pattern fits There are plenty of ways to automate a repetitive workflow, and each comes with a different cost.
• In our experience, it is a good fit when three things are true about the workflow.
• There is a clear starting point, a predictable sequence of steps, and an expected output.
• The third is that user input along the way is acceptable, and often welcome.
• If the workflow needs to run fully autonomously, triggered by an event with no one present, a skill is the wrong tool.

What to take from it

A Claude Code skill paired with an MCP server is a low-infrastructure option, which means it fits some problems well and others poorly. What the skill and MCP server each do It helps to be clear about what each piece does, because the pattern only works when both are present and the responsibilities stay separate. For a problem that fits the three characteristics above, that is more than the problem requires.

Example or evidence

• If the steps are not clear enough to document before you build, the automation will inherit that ambiguity rather than resolve it.
• We build these servers as read-only by design: each one connects only to the systems the workflow requires, with credentials scoped to exactly what it needs.
• A custom agent needs a place to live, an interface for users to interact with it, user management, an orchestration layer to coordinate its steps, and ongoing maintenance as the workflow evolves.
• If the workflow is well-defined and repetitive, the data sources are known upfront, and a human in the loop is acceptable, a skill and MCP server will likely be cheaper to build, easier to maintain, and...

Details worth keeping

It was just slow, repetitive, and easy to get wrong. That monthly report was not an isolated case. The first is that the workflow is well-defined, bounded, and repetitive.

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