Skills vs. Actions in AI Assistants: Understanding the Building Blocks

As more teams experiment with AI-powered assistants, one question keeps coming up: “What’s the difference between a Skill and an Action?”

It’s a smart—and surprisingly common—question. These terms show up in tools like Copilot, Gemini, Rovo, and others, but they’re often used inconsistently. Depending on the platform, a Skill might look like a button, a workflow, or even a full automation. An Action might be something you type, click, or define in code.

The concepts sound similar—but understanding the difference can help you design smarter, more scalable workflows. Let’s break it down clearly, with real-world examples across platforms.

First, Let’s Set the Record Straight:

Skills Didn’t Replace Actions—They Package Them.

In modular AI systems a Skill is a packaged capability built from two or more Actions, plus the logic that ties them together. An Action is still the individual trigger or step. So yes, a Skill can run on its own, but a human typically needs to prompt the Action that activates it.

This pattern follows modular programming, where small units (Actions) are assembled into larger, reusable modules (Skills). It reduces complexity, improves governance, and makes automation scalable.

How Skills and Actions Compare Across Platforms

Concept	Rovo by Atlassian	Microsoft Copilot	Google Gemini
Skill	Modular capability configured in Rovo Studio or built in Forge (e.g., "Create Jira Issue")	Built-in commands like "Summarize email" or connected functions via Graph/Power Automate	Reusable prompt patterns or Workspace-connected tasks (e.g., "Summarize Google Doc")
Action	User-triggered input in Rovo Chat that activates a Skill	Natural language request (e.g., “Draft a reply”) that invokes a Skill	Prompt or button click that activates assistant capabilities
Custom Skills	Created via Forge or connectors; reusable across agents	Custom workflows built in Power Automate or Loop	Limited; custom prompts and API integrations growing
Automation Model	Modular, task-oriented, and RBAC-governed	Often tied to Microsoft 365 context and user permissions	Still emerging; often tied to Google Docs/Sheets UI interactions
Example	“Report outage” → triggers Skills: Create Jira + Post to Slack + Generate Postmortem	“Summarize this Teams thread and email it” → triggers multiple back-end steps	“Create task list from meeting notes” → invokes Gemini + Google Tasks

The takeaway:

While the language varies, the underlying model is consistent:

• Actions are user-initiated

• Skills are capabilities (often composed of smaller functions or steps)

• AI handles the logic and orchestration behind the scenes

Deep Dive: Skills and Actions in Rovo

Skills = Capabilities

In Rovo, Skills are what the agent is able to do:

• Create a Jira issue

• Summarize a Confluence page

• Notify a Slack channel

• Generate a diagram

When you create a new Skill in Forge, you’re assembling existing Actions and adding the logic that ties them together, effectively producing a capability that doesn’t exist in the set of Atlassian-built Skills.

Actions = Triggers

Actions are how those Skills are used. When a user types “Report an outage,” Rovo turns that request into an Action and executes one or more Skills behind the scenes.

Skills = can do Actions = go do it

How to Use Skills + Actions Effectively in Any Platform

Whether you’re working with Rovo, Copilot, or Gemini, the strategy is the same:

1. Define the repetitive workflow

   • What do you want automated? (e.g., triage, postmortems, summaries)

2. Break it into modular tasks

   • Identify the smallest pieces (Actions)

3. Group into reusable units

   • Combine Actions into Skills or automated flows

4. Attach to a trigger

   • A chat input, button click, or API call

5. Pilot, track, and revise

   • Monitor credit usage, accuracy, and adoption

6. Document and reuse

   • Build a Skill/flow library and share across teams

Common Pitfalls (and What to Watch for)

Challenge	Fix This
Confusing Skill vs Action	Clarify that Actions trigger Skills. Use visuals if needed.
Skill overload	Use fewer than 5 per agent or flow to keep behavior tight
Unmonitored AI usage	Track credit usage and set up governance policies
Weak connectors	Ensure data sources (e.g., Confluence, M365, Drive) are integrated
Scaling inconsistency	Design reusable templates, and assign local champions

Why This Matters for Teams

The Skills + Actions pattern helps your org:

✅ Launch faster with repeatable components

✅ Keep AI adoption manageable and visible

✅ Scale without rebuilding everything from scratch

✅ Deliver better user experiences with fewer steps

No matter the tool—Rovo, Copilot, or Gemini—this modular approach wins.

Whether you’re using Atlassian Rovo, Microsoft Copilot, or Google Gemini, the same principle applies: Break complex workflows into small, reusable parts (Actions), combine them into meaningful capabilities (Skills), and expose them through simple triggers.

Understanding how each platform maps these layers will help you:

• Reduce friction

• Build trust in AI workflows

• And scale responsibly