The problem you’re actually solving

You’ve found a prompt or a sequence of steps that works. The next person on your team repeats the experiment from scratch, or worse — you end up with five slightly different versions of the same workflow floating across Slack threads and shared drives. That’s the real cost of not having reusable agent skills: time wasted rediscovering what already works.

OpenAI Skills and Claude Skills were built to fix exactly this. They let you package a proven procedure — instructions, examples, reference files, even scripts — into a reusable unit you can install in different agents or different contexts. But they are not the same thing, and the platform they run on matters more than the format.

What a skill actually is

A skill is not a prompt. It’s a structured bundle that lives alongside the code and reference material it depends on. At minimum, it contains a SKILL.md manifest with a name, description, and instructions. It can also include templates, example outputs, scripts, and assets. The agent sees just the metadata first — the name and description — and only loads the full instructions when it decides the skill applies. OpenAI calls this progressive disclosure, which keeps the agent’s context from being flooded with every procedure in your library.

In practice, this means your team’s working knowledge — the way you handle customer renewal reviews, the conventions your codebase follows, the steps you take when a deployment fails — gets preserved in a form that can be reviewed, versioned, and installed elsewhere. That is the core value: turning tribal knowledge into something durable.

OpenAI Skills: a cross-product standard

OpenAI has made Skills available across ChatGPT Business, Enterprise, and Edu, as well as in Codex and through the Responses API. A single skill package can travel between these surfaces because OpenAI follows the open Agent Skills standard. The files are portable.

There is an important caveat, though. The files travel; the trust boundary does not. A skill that runs inside a Codex sandbox does not carry its sandbox permissions into the API. An API version pin does not create a workspace approval record in ChatGPT. OpenAI has standardized the artifact before it has unified the control plane. Skills are immediately useful, but they require a new discipline: treating agent procedures like software.

Skills are versioned. You can upload a newer version without breaking agents pinned to an earlier one. You set a default version that new agents pick up automatically, and you can explicitly pin to a version number or to latest. This matters for anyone running agents in production — you can update a workflow safely without disrupting what is already working.

They work in two execution modes. Hosted shells run inside an OpenAI-managed container, with skills attached as references. Local shells run on your own machine, with skills pointed to at file paths. The local mode is useful for development: edit the SKILL.md, re-run, and see changes immediately with no upload step.

Claude Skills and the broader ecosystem

Claude’s approach to reusable workflows follows a similar logic but uses a different packaging and integration model. On Claude Code and Claude CLI, skills are typically expressed as structured system prompts rather than zipped bundles with manifests. The underlying idea is the same — capture a repeatable procedure once and reuse it — but the mechanism is lighter and more integrated with Claude’s prompt architecture.

This has a practical consequence for multi-platform teams. OpenAI Skills are not directly portable to Claude CLI or OpenCode. The task decomposition, the prompt patterns, and the embedded scripts transfer as concepts, but someone has to repackage them into the target format. If your stack uses both OpenAI and Claude, you will maintain two parallel skill libraries unless you abstract the logic into something platform-agnostic, such as an MCP server.

Where skills and plugins overlap — and where they diverge

The line between a skill and a plugin is thin, and getting it wrong leads to over-building. Here is the distinction that matters:

A skill tells the agent how to do something. It contains instructions, decision branches, error-handling knowledge, and sometimes supporting files or scripts. It is procedural guidance layered on top of whatever tools the agent already has access to.

A plugin gives the agent a new capability. Through mechanisms like MCP servers, a plugin provides tools — functions the agent can call directly. Reading from a CRM, querying a database, triggering a webhook — these are capabilities the agent does not have natively. A plugin adds them.

In practice, a complete workflow often needs both. The plugin gives the agent access to your tools. The skill tells the agent the right order to use them, which errors to expect, and how to handle edge cases your team has already solved.

The rule of thumb: build a skill when the agent already has the tools and you need to preserve a proven sequence. Build a plugin when the agent is missing the tool entirely.

When to choose skills over plugins — a founder’s decision framework

You do not need to over-engineer this. Start with the pain you are solving:

  • If you keep rewriting the same prompt for the same type of task, you need a skill. A CRM follow-up workflow, a quarterly report analysis, a bug triage sequence — anything that follows a known procedure is a candidate for packaging as a skill.

  • If you keep asking the agent to do something it physically cannot do because the tool does not exist, you need a plugin. Accessing your accounting software, reading from a private API, pushing results to a notification channel — these require adding a new capability, not just codifying a sequence.

  • If you are evaluating whether to invest in skills, ask: would the next person on my team rediscover this workflow from scratch? If the answer is yes, packaging it as a skill saves everyone time going forward.

Skills and plugins are not interchangeable. Skills reduce repetition. Plugins expand capability. The best agent setups use both, and the smart ones start with skills because they require less infrastructure and give immediate returns.

Building your first skill library

You do not need a formal process to start. Pick one workflow your team runs repeatedly — something with clear inputs, a defined sequence, and outcomes you can verify. Write the SKILL.md with a concise description, the step-by-step instructions, and any reference files or examples the agent will need. Test it in a local environment if possible, then share it with the person who would have rebuilt it from scratch.

Version control is essential from the beginning. Skills that live in git are reviewable, diffable, and maintainable. Without versioning, you return to the same fragmented prompt libraries you were trying to escape.

Keep the scope narrow. A skill that tries to handle ten different scenarios becomes a maintenance burden and an agent confounder. One well-defined procedure per skill, and a library that grows organically as new pain points surface.

FAQ

Can I use the same skill library across OpenAI and Claude? No. The formats are not compatible. You can transfer the underlying logic and patterns, but someone needs to repackage each skill for the target platform.

Do skills replace good prompting? No. Skills are a structured way to preserve good prompts and the procedures around them. A skill without clear instructions is still just a prompt in a different container.

Are skills only for technical teams? The packaging may require some setup, but the value proposition is universal: if a task is repeated, it is worth capturing. Even solo founders benefit from codifying workflows that currently live in their head.

What happens when the underlying model changes? Skills are instruction bundles, not model-specific code. They may need revision if a new model interprets instructions differently, but the structure remains useful across model upgrades.

Is there a cost to using skills? The skills themselves are free to create and use within the platforms that support them. The cost comes from the agent runtime — the API calls the skill triggers — but a well-built skill should reduce those calls over time by making the agent more efficient.

Sources