Agent Skills are suddenly everywhere in the AI engineering world, and for good reason. They solve a very real problem: AI agents may be smart, but they still know nothing about your organization unless you explicitly teach them. They do not automatically understand your internal workflows, your service catalog, your production readiness rules, or the exact steps needed to fix recurring issues.
That is where Agent Skills come in. They give your AI agent reusable knowledge, structured instructions, and workflow-specific context so it can do meaningful work instead of acting like a generic chatbot with tool access.
If you have been hearing about skills.md files, MCP servers, Claude, Copilot, and custom agent workflows, this is the missing mental model. Once you get it, the whole ecosystem makes a lot more sense.
Why Agent Skills are getting so much attention
One quick way to understand whether a concept matters is to look at search interest. The term Agent Skills has been climbing fast, especially in recent months. That is usually a sign that people are not just curious, they are actively trying to use something in real projects.
And it makes sense. This is not a niche concept only for AI researchers. Developers, platform teams, engineering managers, and AI engineers can all benefit from it because Agent Skills increase both capability and efficiency for AI agents.
A lot of the early buzz is tied to Claude because Anthropic introduced the concept as an open standard. But the idea is bigger than one model or one company. The important part is that a skill can travel across platforms, which makes it much more useful than a one-off prompt hidden in one tool.
How we got here: from function calling to MCP to Agent Skills
To really understand Agent Skills, it helps to place them in the broader evolution of AI agents interacting with the outside world.
1. Function calling
The first big step was function calling, also known as tool calling. This was when large language models started invoking external tools through a predefined JSON schema. A classic example is something like get weather data for a city.
That was useful, but it had clear limitations:
- Manual wiring everywhere. Every function had to be described and connected by hand.
- Error handling was your job. If something failed, the system did not really know how to recover intelligently.
- Scaling was painful. Every new capability increased developer overhead.
So function calling gave models access to tools, but not much autonomy or reusable workflow intelligence.
2. Model Context Protocol (MCP)
Then came Model Context Protocol, or MCP. This made it much easier to connect AI agents to external tools and data sources through a standard protocol.
The easiest way to think about MCP is as a USB-like standard for AI systems. Instead of custom integrations for every tool, you get a cleaner, more interoperable plug-and-play model. That is why so many companies are now building MCP servers for their own systems and workflows.
MCP was a major leap because it standardized access.
But access alone is not enough.
3. Agent Skills
This is where Agent Skills become important. If MCP gives your AI agent access to external tools and data, Agent Skills teach the agent what to do with those tools and data.
That is the core idea.
Instead of giving an agent only tool access, you package repeatable workflows, domain knowledge, trigger conditions, and repair playbooks into reusable skill files. The agent can then reason through a task in a more structured and specialized way.
Each stage in this evolution shifts more agency from the developer to the system:
- Function calling gave the model tool access.
- MCP standardized access to tools and data.
- Agent Skills gave the model reusable capability and workflow intelligence.
That is why this feels like a truly agentic progression.
What Agent Skills actually are
Agent Skills are folders of instructions and supporting files that package a repeatable workflow, specialized knowledge, or a new capability for your AI agent.
On the surface, that might sound like saved prompts. But they are more than that.
A good skill does not just store text. It defines:
- When the skill should activate
- What the agent should do step by step
- What reference data the agent should use
- What remediation playbooks or actions it can follow
So instead of copy-pasting a giant prompt every time you want an agent to do something specialized, you write that capability once and reuse it across sessions and tools.
This is exactly what makes Agent Skills powerful. They turn a general-purpose model into something much closer to a reliable specialist.
Example: Custom deployment skill
Here's an example of a custom skill for deploying services in your organization:
{
"identifier": "deploy-to-production",
"title": "Deploy to Production",
"properties": {
"description": "Guide for deploying services to production. Use when users ask to deploy, release, or promote a service to production.",
"instructions": "# Deploy to Production\n\nFollow these steps to deploy a service to production:\n\n## Step 1: Verify prerequisites\n\n- Check that all tests pass.\n- Verify the service has a production-readiness scorecard score above 80%.\n- Confirm the service owner has approved the deployment.\n\n## Step 2: Run the deployment\n\nExecute the deployment action for the target service and environment.\n\n**Example input:**\n- Service: `payment-service`\n- Environment: `production`\n\n**Expected output:**\n- Deployment initiated successfully.\n- Action run ID returned for tracking.\n\n## Step 3: Verify deployment\n\n- Check the action run status.\n- Verify the service is healthy in production.\n- Monitor for any alerts in the first 15 minutes.\n\n## Common edge cases\n\n- If tests are failing, do not proceed with deployment.\n- If scorecard score is below threshold, recommend remediation steps first.\n- If deployment fails, check logs and suggest rollback if needed.",
"references": [
{
"path": "references/deployment-runbook.md",
"content": "# Deployment Runbook\n\n## Pre-deployment checklist\n\n- [ ] All CI checks pass\n- [ ] Code review approved\n- [ ] QA sign-off received\n\n## Rollback procedure\n\nIf deployment fails:\n1. Revert to previous version\n2. Notify on-call team\n3. Create incident ticket"
},
{
"path": "references/common-errors.md",
"content": "# Common Deployment Errors\n\n## ImagePullBackOff\nCause: Container registry authentication failed.\nFix: Verify registry credentials.\n\n## CrashLoopBackOff\nCause: Application fails to start.\nFix: Check application logs and configuration."
}
],
"assets": [
{
"path": "assets/deployment-config.yaml",
"content": "apiVersion: apps/v1\nkind: Deployment\nmetadata:\n name: {{ service_name }}\nspec:\n replicas: 3\n strategy:\n type: RollingUpdate"
}
]
}
}
The open standard behind Agent Skills
Agent Skills were originally created by Anthropic and released as an open standard on December 18, 2025, along with the specification and SDK. The standard is now governed as a cross-platform specification at agentskills.io.
The practical implication is huge. A skill created for Claude is not trapped inside Claude. The same skill can work across multiple AI platforms that adopt the standard, including tools like OpenAI Codex, Gemini CLI, GitHub Copilot, Cursor, VS Code, and others.
That portability is what makes this more than another product feature. It is infrastructure for reusable agent behavior.
Why LLMs need Agent Skills in the first place
LLMs are great at general conversation, brainstorming, and broad reasoning. But when workflows become complex, they often become inconsistent. They forget details, miss edge cases, or answer too generically because they do not have the right context.
This becomes painfully obvious in cases like:
- Analyzing internal service health
- Understanding organization-specific scorecards
- Applying a company's engineering rules
- Generating precise remediation steps
- Working across tools like GitHub, issue trackers, and internal platforms
Agent Skills help bridge that gap. They move the model from passive chat behavior to active, specialized execution grounded in your real systems and workflows.
A practical example: building an Agent Skill with Port.io
To make this concrete, consider a real workflow built around Port.io.
Port is an agentic internal developer platform that helps teams automate engineering workflows. It acts as a central place where developers can see services, ownership, scorecards, readiness, and other operational data without bouncing between a dozen different tools.
In this example, Port's MCP server is connected so the AI agent can access live data from a Port account. Once connected, the agent can pull information such as:
- Services in the catalog
- Blueprints in the organization
- Production readiness states
- Scorecard pass/fail data
That gives the agent raw access. Then Agent Skills provide the behavior and context needed to make that access useful.
The three-file structure of this Agent Skill
The example skill is built around a production readiness workflow and uses three main files.
1. skills.md
This is the brain and trigger mechanism of the skill.
It includes:
- The skill name
- Description
- Metadata like author and version
- Activation keywords
- Instructions for how the agent should behave
In this case, the skill is focused on Port readiness. The description includes keywords such as scorecard, level B, and branch protection so the agent knows when to activate the skill.
It also defines the workflow for diagnosing failures, understanding readiness levels, generating PR descriptions, and suggesting fixes.
2. references/scorecard-state.md
This file contains the factual reference data.
It acts like a snapshot of the actual Port catalog, including the current state of services and scorecard rules. In the example, it includes data for six services and their pass/fail status against readiness rules.
This matters because it stops the agent from answering in vague terms. Instead of saying, "You may need better branch policies," it can say, "This specific service is failing because branch protection is missing and no recent PR activity exists."
3. assets/fix-checklist.md
This file is the remediation playbook.
It gives the agent a step-by-step checklist for fixing failures, such as:
- Assigning the correct team
- Enabling branch protection
- Setting code owners
- Ensuring recent PR freshness
So if the reference file tells the agent what is wrong, the checklist tells it how to fix it.
What this skill enables the agent to do
Once these files are in place and the Port MCP server is connected, the AI agent becomes dramatically more useful.
It can answer questions like:
- What services are in my Port catalog?
- What blueprints exist in my organization?
- Why is the travel service failing its scorecard?
- Which service is closest to reaching level B?
- Write a PR description for Agentic AI explaining the readiness impact.
- Assign Agentic AI to the AI team.
And importantly, it can answer these without forcing you to paste all the context into every new conversation.
That is the practical magic of Agent Skills. Context is packaged once, then reused repeatedly.
Understanding Port readiness in this example
The skill in this setup revolves around production readiness in Port.
Port readiness is basically a grading system that tells you how production-ready a service is. The levels include things like A, B, C, and F, depending on how many scorecard rules are satisfied.
In the example workflow, several services are currently at level C. The agent can inspect the rules, explain why a service is still at level C, and tell you what must be done to move it up to level B.
Typical requirements for moving from level C to level B include:
- Assigning a team
- Enabling GitHub branch protection
- Pushing a recent PR
Because the skill has both the scorecard state and the remediation checklist, it can map those rules directly into actionable next steps.
How the interaction feels in practice
After connecting the MCP server and loading the skill into a coding environment like GitHub Copilot agent mode in VS Code, you can work conversationally.
You can ask:
- Why is the prompt engineering service failing?
- What team is assigned to this service?
- How can all my services reach level B?
- Can you push a simple PR to this service?
The agent then checks the skill instructions, pulls the relevant facts from the reference file, uses the checklist for remediation guidance, and responds in a way that is specific to your setup.
In the example, the agent can even update team assignments in the scorecard state and suggest exact actions needed to improve readiness.
This is a big shift from normal chatbot usage. Instead of asking broad questions and getting broad answers, you are interacting with an agent that understands your environment and your operational rules.
Why this is more powerful than prompts alone
A long prompt can tell an agent a lot of things once. But it is still fragile.
Prompts are easy to lose, hard to standardize, and difficult to reuse cleanly across teams and platforms. They also tend to degrade over time as workflows evolve.
Agent Skills solve that by separating responsibilities:
- The skill file defines behavior and triggers
- The reference file provides facts and current state
- The checklist file provides action plans
That structure makes the whole system more maintainable, shareable, and predictable.
It also makes it easier to build agents that do not just know tools, but know how your organization actually works.
The bigger takeaway
The important idea here is not just Port, Claude, or one specific tutorial setup. The bigger takeaway is that Agent Skills are a reusable layer of organizational intelligence for AI agents.
You can imagine applying the same pattern to many other internal workflows:
- Incident triage
- Release readiness
- Security policy checks
- Onboarding flows
- Documentation enforcement
- Infrastructure review
As long as the agent has access to the right tools and data through something like MCP, skills can teach it how to reason and act within that domain.
What makes Agent Skills so compelling right now
There are three reasons Agent Skills feel especially important right now.
- AI agents are everywhere, but most are still generic.
- MCP gives agents access, but not domain behavior.
- Teams need reusable workflows, not prompt improvisation every time.
That combination creates the perfect environment for skills to become a foundational pattern.
If the first wave of AI was about generating text, and the second wave was about calling tools, this next wave is about packaging expertise so agents can repeatedly perform meaningful work.
Final thoughts
Agent Skills are one of the clearest signs that AI tooling is maturing from demos into operational systems.
They let you encode workflows once, connect them to real systems, and reuse them across platforms. In practical terms, that means your AI agent can stop acting like an outsider and start behaving like a teammate who understands your stack, your rules, and your goals.
That is the real leap here.
MCP gives your agent the keys. Agent Skills teach it how to drive.
If you want to explore this approach hands-on, the Port-based production readiness example is a great model: connect your data source, define the skill behavior in skills.md, add factual reference state, add a remediation checklist, and then let the agent work against your real environment.
Once you see that flow in action, it becomes obvious why Agent Skills are getting so much attention.
BTW, Try Port for Free!
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