How one agency packaged years of expertise into reusable Claude AI skills

A small digital marketing agency owner found that relying on general-purpose AI with one-off prompts failed to preserve the accumulated procedural knowledge behind recurring tasks like SEO audits and client reports. The team identified a key distinction between three building blocks: skills (self-contained, judgment-carrying capabilities for a single task), MCP connectors (protocol-based integrations that give the model access to external services), and agents (end-to-end workers that combine skills and connectors). Early attempts to bundle all three into a single large prompt proved unwieldy, but separating them made each component easier to audit, test, and trust. The agency then built installable skill folders that load on demand, encoding not just instructions but the hard-won logic behind decisions — such as which schema types earn rich results or how to structure reports for non-technical clients. The approach, compatible with Claude and other MCP-supported models, is presented as a replicable framework for teams looking to systematize expert workflows in AI tooling.