Stop Giving AI Agents More Prompts. Give Them Skills.

Alex Shevchenko — Wed, 13 May 2026 22:32:32 +0000

I used to think better AI agent results mostly came from better prompts.

Longer instructions. More examples. More constraints. A cleaner system prompt.

All of that helps.

But after using coding agents, terminal workflows, and automation tools every day, I think the bigger unlock is simpler:

Stop making the agent rediscover the workflow every time. Give it a skill.

A prompt tells the agent what you want.

A skill teaches the agent how the work should be done.

That difference matters a lot once you move past demos.

The problem with “just prompt it better”

Most agent failures I see are not because the model is too dumb.

They happen because the workflow is unclear.

You ask the agent to do something like:

Process these videos for social media.

The model can probably figure out a path.

Maybe it uses FFmpeg.
Maybe it exports the right format.
Maybe it remembers to normalize audio.
Maybe it generates thumbnails.
Maybe it names the files consistently.

But “maybe” is the problem.

If the work matters, you do not want the agent improvising the process every time.

You want the agent to follow a repeatable workflow.

That is where skills become useful.

Tools are not workflows

Giving an agent tool access is powerful, but it is still too low-level.

There is a big difference between:

The agent can run FFmpeg.

and:

The agent knows how to turn raw clips into 1080x1920 Shorts with trimmed intros, normalized audio, watermarks, thumbnails, and predictable output folders.

The first one is a capability.

The second one is a workflow.

Developers often underestimate this gap because we are used to stitching tools together in our heads.

Agents need that stitching written down.

Not because they cannot reason.

Because repeatable work should not depend on fresh reasoning every time.

What I mean by a skill

A useful agent skill is not just a script.

It usually includes:

when to use it
when not to use it
required tools
expected inputs
step-by-step workflow
default settings
failure cases
output format
verification steps

In other words, a skill packages judgment.

It turns tribal knowledge into something the agent can reuse.

A good skill says:

For this type of task, use this pattern.
Avoid these traps.
Check these outputs before saying done.

That is much more valuable than another paragraph of vague prompting advice.

A real example: video processing

Suppose I need to batch-process 20 short videos.

Without a skill, I might prompt the agent like this:

Use FFmpeg to trim the clips, normalize audio, resize them for vertical video, add a watermark, export MP4, and create thumbnails.

That can work once.

But the next time, the agent may choose slightly different flags, skip an edge case, or name files differently.

With a skill, the workflow becomes stable:

terminal-skills install ffmpeg

Now the agent has a defined operating pattern for media conversion, editing, audio handling, and batch processing.

The skill does not make FFmpeg more powerful.

It makes the agent less random.

That is the point.

Another example: Claude Code workflows

The same idea applies to coding agents.

A raw coding agent can read files, edit code, run tests, and open pull requests.

But real teams need more than that.

They need conventions:

how to inspect the repo first
when to run tests
how to avoid dangerous git commands
how to split tasks between agents
how to verify the final state
how to report blockers clearly

That is why skills like these are interesting:

terminal-skills install claude-code
terminal-skills install git-guardrails-claude-code
terminal-skills install coding-agent

The value is not “Claude can code.”

The value is that the agent gets a safer, more repeatable way to work inside a real development flow.

MCP connects. Skills direct.

MCP is getting a lot of attention right now, and for good reason.

It gives agents a cleaner way to connect to tools, data, and external systems.

But connection is not the same as execution.

An MCP server can expose a capability.

A skill tells the agent how to use that capability in a useful workflow.

That is why I like this framing:

MCP gives agents hands. Skills give them habits.

Hands are necessary.

Habits are what make the system reliable.

Why this matters for developers

If you are building with AI agents, you probably already have repeated tasks hiding in your workflow.

Things like:

setting up a new project
generating boilerplate
reviewing a PR
processing media
creating documentation
deploying a small app
auditing dependencies
cleaning data
testing an API

If you prompt these from scratch every time, you are paying the model to rediscover your workflow.

Instead, write the workflow down once.

Turn it into a skill.

Then let the agent reuse it.

The test I use

Before I turn something into a skill, I ask three questions:

Do I do this task more than once?
Does the task have a preferred sequence?
Would mistakes be annoying, expensive, or time-consuming?

If the answer is yes, it probably should not live only in a prompt.

It should become a reusable workflow.

That can be a shell script.

It can be a SKILL.md file.

It can include docs, config, examples, guardrails, or helper scripts.

It can be an installable package.

The format matters less than the behavior:

the agent should know what “done correctly” looks like.

Why I’m building around this

This is the idea behind Terminal Skills.

The goal is not to make agents sound smarter.

The goal is to equip them with reusable workflows for real work:

CLI tools
coding agents
video workflows
MCP servers
automation stacks
devops tasks
content production
data processing

You browse a skill, install it, and give your agent a better operating pattern.

terminal-skills install terminal-skills

That meta-skill teaches the agent how to search the catalog and install the right skill for the task.

After that, the agent does not need a long explanation every time.

It has a way to find the right workflow.

The bigger shift

I think the next phase of AI agents is not just better models.

It is better packaging.

The winning systems will not be the ones with the longest prompts.

They will be the ones with the best reusable workflows.

Because once agents are good enough to use tools, the question changes.

It is no longer:

Can the agent do this once?

It becomes:

Can the agent do this correctly every time?

That is where skills matter.

Prompts are good for intent.

Tools are good for capability.

Skills are good for repeatability.

And repeatability is where AI agents start becoming useful in real work.

I’m collecting examples of this pattern at Terminal Skills:

https://terminalskills.io

AI-assisted. Human reviewed.

Forem: Alex Shevchenko