Agent skills are the new workflow layer.

A general chat model is useful. A model with skills is operational.

By skill, we mean a reusable way for an agent to do a specific kind of work: search a CRM, draft a follow-up, check a calendar, prepare a proposal, summarize a call, review a lead, or build a weekly brief. The skill contains the instructions, tool choices, examples, stopping rules, and output format.

That is the workflow layer starting to form around agents.

Why skills matter

Most companies do not need one giant agent that can do everything. They need small, dependable capabilities that can be combined. One skill for lead intake. One for quote follow-up. One for review requests. One for prospect research. One for weekly owner reporting.

This is close to how good teams already operate. Nobody expects one employee to improvise the whole business from memory. They use checklists, SOPs, software, examples, templates, and escalation paths. Agent skills are the AI-native version of that.

The stack is moving this way

OpenAI's Agents SDK centers concepts like tools, handoffs, and guardrails. Anthropic's Model Context Protocol points toward a standard way to connect AI systems to business data and tools. Google's A2A work points toward agents collaborating across systems instead of staying trapped in one vendor's app.

The pattern is bigger than any one platform: agents need interfaces. They need to know what they can use, who they can hand off to, what they are allowed to change, and how their work gets inspected.

What a useful skill includes

• A plain-English job: what this skill is for and what good output looks like.
• Allowed tools: exactly what the agent may read or write.
• Inputs: what the workflow needs before it starts.
• Examples: real before-and-after samples from the business.
• Boundaries: when the agent must stop and ask for approval.
• Audit trail: what the system records so a human can understand what happened.

That last part is underrated. If nobody can see why the agent made a recommendation, the business will not trust it for long.

The MDI version

For MDI LABS, skills are not just a developer feature. They are how we package operational knowledge. A law intake skill should not behave like an HVAC estimate skill. A real estate follow-up skill should not behave like an insurance claim triage skill.

The more precise the skill, the less the system depends on model magic. That is good. Magic is hard to operate. Workflows can be improved.