I noticed something odd while working with AI coding agents: I was not only prompting them. I was slowly teaching them my habits.
How I review Python snippets. How I prepare a newsletter. How I want blog posts to sound. How I prefer Markdown to be preserved. How do I check that an AI has not enthusiastically “improved” something that was already fine?
At some point, repeating those instructions stopped feeling like prompting and started feeling like maintaining tiny workflow manuals.
That is where AI skills come in.
AI Skills Defined: Reusable Workflow Packages for AI Assistants and Agents
AI skills are reusable instruction packages for AI assistants and agents.
Think of AI skills as a small “how we do this task” folder. Instead of pasting the same long prompt every time, you turn the workflow into a skill once, then the AI can reuse it when the task comes up.
A skill usually contains:
my-blog-newsletter-skill/
├── SKILL.md # main instructions
├── examples/ # good examples
├── templates/ # output templates
├── scripts/ # optional helper code
└── references/ # style guide, rules, schemas, checklists
The important file is usually SKILL.md. It explains what the skill does, when to use it, what inputs are needed, the steps to follow, the output format, and final checks. OpenAI describes ChatGPT skills as official reusable workflows built from instructions, examples, and code for recurring tasks, and Claude/Microsoft use a very similar idea: folders of instructions, scripts, and resources loaded only when relevant. ([OpenAI][1]) ([Claude Help Center][2]) ([Microsoft Learn][3])
Think of skills as the rung between one-off prompts and fully automated agent pipelines — the place where ad-hoc instructions graduate into something reusable, versionable, and eventually composable into larger workflows.
What Problem Do AI Skills Solve: Eliminating Repeated Workflow Explanation
AI skills solve the “I keep explaining the same thing again” problem.
For example, you might often ask an AI to:
“Please rewrite this in my blog style, use short paragraphs, add a practical example, keep the tone warm, preserve Markdown, avoid hype, add SEO title suggestions…”
That is a perfect skill.
Instead of repeating all that, you create a blog-post-polisher skill. Then you can simply say:
Use my blog polishing skill on this draft.
Or the assistant may detect it automatically, depending on the platform.
Skills vs Prompts vs Custom GPTs vs MCP Tools: A Comparison
This is the clean mental model:
| Thing |
Best for |
Example |
| Prompt |
One-off instruction |
“Summarise this article in 5 bullets.” |
| Custom instructions |
Always-on preferences |
“Use British English.” |
| Skill |
Repeatable workflow |
“Turn rough notes into a newsletter draft using my format.” |
| Custom assistant (Custom GPT, Gem) |
A specialised persona tied to a platform |
“My editorial assistant for AI blog posts” in ChatGPT or Gemini. |
| Custom agent |
A programmatic agent with tools and logic |
A multi-step agent that drafts, reviews, and publishes automatically. |
| MCP tool/server |
Giving the AI access to external systems |
Read files, update CSV tasks, query GitHub, fetch analytics. |
A skill is not just a prompt. It is more like a mini operating manual for a task.
Microsoft’s Agent Skills docs describe them as portable packages of instructions, scripts, and resources that give agents specialised abilities. VS Code’s Copilot docs also distinguish skills from custom instructions: custom instructions are mostly coding guidelines, while skills can include scripts, examples, and reusable workflows. ([Microsoft Learn][3]) ([Visual Studio Code][4])
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