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RecordedMay 28, 2026

Building with Liatrio | Agent Skills: Turning AI workflows into reusable team capabilities.

Explore how Agent Skills transform repeatable AI workflows into reusable, installable, and governable team capabilities, and why they are emerging as a more powerful foundation than prompt libraries for scaling AI-native engineering practices across organizations.

Presenters

Damien Storm

Damien Storm

Lead AI Enablement Engineer

Robert Kelly

Robert Kelly

VP of Innovation

Your Team Has Great AI Workflows. They Just Live on One Person's Laptop.

Most teams have at least one person who's figured it out. They've got a review workflow that actually catches issues, a browser QA routine that leaves evidence behind, or a spec workflow that keeps the agent from wandering. Their teammates know it, too.

But ask them to document it. Ask them to package it so the rest of the team can use it the same way, in a new session, on a different machine. That's where it falls apart. The workflow lives in their head. The prompts are scattered across local files. Someone else on the team is doing the same thing completely differently, and neither approach has made it into any shared system.

This is the problem Robert Kelly, VP of Innovation at Liatrio, and Damien Storm, Lead AI Enablement Engineer, set out to solve in Liatrio's latest AI-native webinar: how do you take the AI workflows that are actually working and make them repeatable at the team level?

Their answer is agent skills.

A skill is just a folder

That's worth repeating, because the name sounds heavier than it is. A skill is a folder. At the center of it is a SKILL.md file with a name, a description, and the core instructions. The description is not just documentation. It is routing metadata that helps the agent understand what the skill does and when to use it.

From there, you can layer in as much or as little as the workflow needs. A references/ folder holds deeper documentation, things like CLI tool specs or conventions the agent needs to follow for a specific task. A scripts/ folder is where you put deterministic code, Python, Node, Bash, whatever runs the same way every time, so the agent isn't generating the same logic from scratch on every invocation. Templates or assets lock in reusable output shapes: the same HTML report structure, the same JSON contract, the same folder layout for a new repo.

The important part is progressive disclosure. The agent sees the skill name and description first. If the task matches, it loads the full SKILL.md. If the workflow needs more detail, it can pull in the supporting files on demand. The whole playbook does not have to sit in the context window for every task.

Skills are also portable. At this point, skills are basically universal across the major agent harnesses. The exact install path may differ from tool to tool, but the pattern is the same: a simple folder-based package for reusable agent workflows.

Why this is different from an MCP

MCPs and skills solve different problems, and Damien spent time on this distinction in the webinar because it trips people up.

MCP gives an agent access to external tools, services, data, and actions. A skill teaches the agent how to run a workflow, often using those tools. Put differently: MCP gives the agent tools. Skills teach it the team's way to use them.

Skills also help with context because they load progressively. The agent does not need every checklist, convention, template, and example in the window on every turn. It can keep a small index of skill names and descriptions available, then load the full playbook only when the task calls for it.

Damien's phrase for it stuck: skills protect the context window by loading the playbook only when the task calls for it.

Where to start

One skill. Not a full marketplace, not a complete catalog of your team's workflows. One workflow that already works, packaged as a skill, committed to a shared repo, and adopted by the team.

The next step isn't getting better at one-off prompts. Everyone's past that. The next step is writing down how you work so it can be shared, tested, and given to agents and teammates in a way that's actually safe and repeatable.

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