How to Set Up ChatGPT and Codex Workflows Your Team Can Reuse

If the same instructions keep getting typed into AI, the problem is probably no longer prompting.

It is a workflow that should become reusable.

That is the useful way to think about Skills in ChatGPT and Codex. If a prompt is moderately complicated, has multiple steps or conditions, gets reused, or is simply tedious to reconstruct, it is a strong candidate to become a Skill rather than remaining a one-off conversation.

OpenAI describes Skills as reusable workflows that can contain instructions, examples, and code. Codex can use a Skill when it is explicitly requested or when the task matches the Skill's description closely enough for automatic selection.

The important part is not just creating the Skill.

It is creating it from the way the work is actually performed, separating reusable instructions from project-specific information, and allowing the workflow to improve through real use.

Create Skills While Doing Real Work

A useful Skill usually should not be invented in isolation before the underlying work has been performed.

The best time to create one is while working on something that has already been done before.

At that point, the operator already knows the criteria, the preferred process, the expected output, and the mistakes the AI should avoid.

That is when AI can be asked to turn the repeated process into a Skill.

This also creates an immediate real-world test. The task that motivated the Skill is already in progress, so the Skill can be used immediately and checked against a known standard.

A separate artificial testing exercise is often unnecessary because the operator already knows what good output looks like.

If You Reuse the Prompt, Consider Turning It Into a Skill

A practical threshold is simple.

A prompt probably deserves to become a Skill when:

• It has been used more than once.
• It contains multiple steps.
• It has conditions or branching logic.
• It includes important safeguards.
• It requires a specific output format.
• The AI is frequently corrected in the same ways.
• It is annoying to reconstruct.
• Several small requirements have to be remembered every time it runs.

There is no need to turn every one-sentence request into a Skill.

But once a prompt reaches moderate complexity and is likely to recur, turning it into a reusable workflow stops the user from acting as the permanent storage system for the instructions.

Build the Skill From Conversation History

One of the most useful ways to create a Skill is to have AI review previous conversations involving the same task.

Instead of requiring a person to remember every instruction that has ever been given, AI can find previous instances of the workflow and identify the repeated requirements.

Corrections are especially valuable because they reveal requirements that were not obvious in the original prompt.

A useful instruction is:

Review previous conversations where this workflow was performed. Identify repeated instructions, corrections, requirements, safeguards, tools, verification steps, definitions of done, prohibited actions, and output preferences. Turn those repeated patterns into a reusable Skill. Do not include project-specific names, paths, data, or scenarios.

That produces a Skill based on how the work has actually evolved rather than whatever version of the workflow happens to be remembered at the moment.

Tell the AI What to Look For

When AI creates a Skill from previous work, the initial request should explicitly tell it which categories of information matter.

At minimum, it should look for:

• The steps in the workflow.
• Preconditions or prerequisites.
• Required tools.
• Safeguards.
• Things the AI should never do.
• Definition of done.
• Verification steps.
• Required output structure.
• Repeated user preferences.
• Common corrections.
• Edge cases.
• Conditions that change how the workflow should operate.
• Reusable helper code or scripts.

If something important is unclear, the AI should ask.

But it should not overwhelm the user with a 25-question interview.

Keep the questions basic and easy to answer. Ask only what is actually necessary. Simple questions and yes-or-no questions are often enough to resolve missing rules.

The point of the Skill is to reduce work for the user, not create another form the user has to fill out.

Never Hardcode a Specific Project Into a Reusable Skill

One of the biggest mistakes in reusable Skills is accidentally preserving details from the project that happened to be open when the Skill was created.

That defeats the point.

A reusable Skill should be designed more like a reusable function.

Nothing should be unnecessarily hardcoded.

Do not put things like these into a general-purpose Skill:

• A specific client's name.
• One repository name.
• A particular site's URL.
• A hardcoded local project path.
• A specific product.
• One-off business data.
• Instructions that only make sense for the current project.

The Skill should describe the reusable process.

Project-specific context should come from the current project, the repository, the conversation, or project-level instructions when the Skill runs.

A useful rule is:

If changing projects makes an instruction stop making sense, it probably does not belong in the reusable Skill.

Keep Helper Functions With the Skill, Not With an Old Project

Skills can include supporting code and scripts. OpenAI's documentation notes that Skills can include instructions, examples, code, and supporting resources.

This creates another easy mistake.

AI may create a helper script while working inside Project A, leave that script inside Project A's directory, and then reference it from the Skill.

Everything appears to work until the Skill is used on Project B.

The supposedly reusable Skill then reaches backward into Project A and tries to run a helper function from the old project's files.

Now the workflow secretly depends on an unrelated repository.

For a Skill intended to work globally across projects, reusable helper functions should live with the Skill itself or in another deliberate global library of reusable functions.

Do not make a global workflow depend on the directory where it happened to be created.

Make the Skill Self-Maintaining

The initial version of a Skill should not be treated as frozen forever.

If a new instruction or correction appears while the Skill is being used, and that instruction is likely to apply to future uses of the same workflow, the Skill should be updated.

For example, imagine a Skill produces the correct output but repeatedly formats one section in an undesirable way.

The correction might be:

Do not present this part as a paragraph. It should always be an ordered list.

If that rule clearly applies every time the Skill runs, it should not need to be repeated forever.

The Skill should absorb it.

The same applies to safeguards, verification requirements, tool preferences, naming conventions, output structures, or new workflow steps.

This is what a self-maintaining Skill means in practice.

It does not mean the agent should randomly rewrite its own operating procedure.

It means repeated user feedback that clearly represents a durable workflow requirement should be incorporated into the reusable instructions.

The workflow should become more accurate to the real process as it continues to be used.

Use Global Instructions for Rules That Apply Everywhere

Not every repeated instruction belongs in a Skill.

Some instructions apply across practically every Skill, project, and coding task.

Those belong at the global agent level.

Codex supports persistent AGENTS.md instructions, including repository-level instructions and broader persistent context.

A practical rule is:

If the same behavior has to be corrected across unrelated projects, it should probably become a global instruction.

Examples might include:

• Communication preferences.
• Commands that should not be run automatically.
• Environment assumptions that are consistently true.
• General scope-control rules.
• Global safety requirements.

Those are not instructions for one workflow.

They describe how the coding agent should behave generally.

If the same correction appears across multiple repositories, that is a sign the rule belongs in global configuration instead of another prompt.

Think in Layers

The cleanest setup is to separate instructions by how broadly they apply.

Global Agent Instructions

These are rules that should apply almost everywhere.

Examples:

• Communication preferences.
• Commands the AI should never run.
• Environment assumptions that are usually true.
• General scope-control rules.
• Global safety requirements.

Reusable Skills

These contain a repeatable workflow.

Examples:

• Performing a particular type of SEO audit.
• Migrating a certain category of data.
• Reviewing accessibility.
• Running a recurring QA workflow.
• Creating a standard deliverable.
• Performing the same multi-step coding operation across different projects.

Project or Repository Instructions

These explain the current project.

Examples:

• Architecture.
• Repository structure.
• Project-specific commands.
• Business logic.
• Current dependencies.
• Local testing requirements.
• Project-specific exceptions.

OpenAI has described AGENTS.md as persistent repository context and recommends keeping instructions useful and focused rather than turning one giant file into an encyclopedia.

That separation matters.

The reusable workflow should not need to know which project it is working on until it is actually invoked inside that project.

Explicit Invocation Should Be Optional

When the correct Skill is known, it can simply be requested by name.

But a well-described Skill should not always need to be named manually.

Codex supports automatically selecting Skills based on the task at hand.

That is useful because eventually the Skill becomes part of the environment rather than something the user has to consciously remember every time.

The Skill description needs to make its purpose clear enough that the AI can correctly determine when it applies.

Handle Conflicts Instead of Silently Guessing

Reusable instructions eventually run into exceptions.

A current user request may conflict with something inside a Skill.

When the answer is genuinely unclear, the correct behavior is to surface the conflict rather than silently guessing.

There is also some responsibility on the operator. If a Skill says to do something one way and the current request intentionally asks for the opposite, that existing workflow should be considered when the new instruction is written.

The goal is not to create an impossible system where AI magically resolves every contradiction.

It is to make durable defaults clear and surface meaningful conflicts when they occur.

Verification Is Mostly About Scope

When Codex finishes the task, the most basic verification requirement is straightforward:

Did it do what the user asked?

And equally important:

Did it do anything the user did not ask for?

AI coding agents can be very capable, which also means they can sometimes decide to “help” by expanding the scope.

That is not automatically desirable.

The completion check should confirm:

1. Every requested requirement was completed.
2. The output matches the user's requirements.
3. Nothing outside the requested scope was unnecessarily changed.

OpenAI also recommends reviewing agent-generated code before integration and provides mechanisms such as diffs, terminal logs, and test results to inspect agent work.

Verification should not be limited to asking whether the code runs.

Scope correctness matters too.

A working solution that modified five unrelated things is not necessarily a correct solution.

A Practical Process for Creating Reusable AI Workflows

A practical process looks like this:

1. Work on the task normally until it becomes clear that the workflow has happened before.
2. If the repeated prompt is moderately complicated, multi-step, conditional, or annoying to recreate, decide that it should become a Skill.
3. Ask AI to review previous conversations involving the same workflow.
4. Tell it to identify repeated instructions, requirements, corrections, safeguards, tools, prohibited actions, verification steps, definitions of done, and output preferences.
5. Tell it to remove all project-specific information.
6. Have it ask only the minimum necessary follow-up questions to resolve genuine ambiguity.
7. Create the reusable Skill globally rather than tying it to the current project.
8. Put any reusable helper code with the Skill or in a deliberate global helper library.
9. Use the Skill immediately on the task that was already in progress.
10. Review the result naturally against the process that is already understood.
11. When a correction should apply to future runs, update the Skill.
12. If the same behavior is repeatedly corrected across multiple unrelated Skills or projects, promote that instruction to global agent instructions instead.
13. Keep project-specific architecture, commands, business logic, and exceptions at the project or repository level.
14. On completion, verify that the agent did everything requested and did not make unrelated changes.

This creates a hierarchy where each piece of information has somewhere sensible to live.

The conversation stops being the permanent storage location for the working process.

The Goal Is to Stop Re-Teaching the AI

The biggest value of this approach is not that Skills are technically impressive.

It is that the same working method does not have to be taught to AI over and over again.

Every repeated correction is a signal.

Every repeated multi-step prompt is a signal.

Every recurring cross-project behavior that has to be corrected is a signal.

Convert repeated workflow instructions into Skills.

Convert repeated cross-project behavior corrections into global agent instructions.

Keep project-specific information inside the project.

Then let the reusable layers improve as the real workflow evolves.

That is how ChatGPT and Codex become substantially more useful over time: not because every prompt gets longer, but because fewer instructions need to be repeated at all.