Over the past few years, people have become skilled at prompting AI. Teams maintain prompt libraries, and individuals keep instructions that consistently produce good results. Some prompts evolve into repeatable workflows used dozens of times each week.
But these workflows are fragile. Prompts are scattered across documents, Slack threads, and personal notes. Reusing them usually means copying instructions into a new conversation and hoping nothing important gets missed.
This is the gap Skills are designed to close.
What makes Skills notable is not just what they do inside ChatGPT or Claude, but the framework they belong to. Anthropic documented Skills for Claude as structured packages containing instructions, metadata, and optional resources that a model can load when needed. OpenAI says its implementation in ChatGPT follows the same Agent Skills open standard.
That matters because it makes Skills bigger than a single product feature. They are designed as reusable workflow packages rather than platform-specific customisations. In principle, that means a skill can move between systems like Claude and ChatGPT, even if the way people create and use them differs from one platform to another.
This is also where Skills diverge from something like custom GPTs. A custom GPT is built for ChatGPT and stays within OpenAI’s ecosystem. A Skill is closer to a portable workflow format: a structured way to package instructions, context, and supporting material so compatible systems can use them.
That broader context matters. Neither Anthropic nor OpenAI is presenting Skills as a one-off prompt feature. Both are moving toward a model where useful workflows can be packaged, reused, and shared in a more durable way.
OpenAI describes Skills in ChatGPT as reusable, shareable workflows that help the model perform tasks more consistently. A skill can include instructions, examples, supporting resources, and code. Once installed, ChatGPT can automatically use one or more skills when they are relevant to the task.
"In simple terms, a good prompt can become something more durable."
Skills make that structure easier to keep.
Instead of rebuilding instructions each time, the workflow becomes part of the system. The model can recognise when that workflow applies and use it without the user having to reconstruct the entire prompt.
That matters because one of the biggest frustrations with AI is inconsistency. The model might produce an excellent result one day and a mediocre one the next, even when the task looks similar. Often the difference is not the task itself, but the quality and structure of the instructions. Skills are a way of preserving that structure, so useful workflows do not remain trapped in documents, chat threads, or someone’s memory.
The idea becomes clearer through simple examples. A team might create a skill for writing case studies in a consistent brand voice. Another might define how customer research should be analysed and summarised for non-technical stakeholders. Another might package a workflow for producing internal reports or recurring briefs.
The point is not to make AI feel more impressive. It is to make useful results easier to reproduce.
This is where the concept becomes practical. In ChatGPT, users can create a skill in conversation, manage skills through an editor, upload them from a computer, and install skills shared within a workspace. Claude has approached the same underlying idea through its own Skills architecture and documentation.
That matters because most AI workflows inside organisations are still informal. Everyone has their own prompts and their own methods. Teams often end up rebuilding the same processes repeatedly.
Skills offer a way to turn those individual habits into shared workflows. Instead of living in lots of documents or personal notes, a good process can be packaged once and reused across a team.
The standards aspect is what makes this especially interesting. OpenAI says its Skills follow the Agent Skills open standard, even though they do not yet sync automatically across products. That detail suggests something larger than a single feature release.
If Skills were limited to one interface, they would still be useful. But if the format becomes widely supported, Skills start to look more like a shared structure for operational knowledge across AI systems.
Both Anthropic and OpenAI also appear to be treating Skills as infrastructure rather than a novelty. The feature is being positioned for business, education, and more advanced use cases, with controls around how skills are enabled, shared, and managed within organisations.
Those are the kinds of controls usually built for tools expected to become part of real workflows.
And that may be the most important part of the story. Skills matter not because the idea is entirely new, but because it is becoming practical at scale.
What we are seeing is not the birth of Skills, but their move into a more mainstream phase. A practice that once sat mostly with developers and advanced users is becoming part of the normal interface of AI tools.
That matters because AI adoption rarely fails on first use. It fails when good results cannot be repeated or shared. Skills address exactly that problem. They turn rushed prompting habits into reusable systems and make reliable workflows easier to preserve.
In that sense, Skills are less about improving prompts and more about capturing how work gets done.