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What a Codex skill is—and what it is for
A skill is a reusable set of instructions and supporting resources for a task. It is organized as a directory with a required SKILL.md manifest, which contains metadata and the workflow. Optional files can supply reference material, scripts, templates, or other assets. See OpenAI’s Skills guide and skill-building guide.
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Use a skill when you repeat a recognizable task and want Codex to approach it consistently—for example, turning meeting notes into a specified project-update format. A skill is not a general-purpose instruction dump: if it tries to cover several unrelated jobs, its description and steps become harder to apply reliably.
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Write down four things first: the task, the situation that should trigger it, the input it needs, and what a successful result looks like. This prevents the common mistake of starting with a folder of instructions before deciding what work those instructions are meant to improve.
- Task: one outcome Codex can produce, such as converting meeting notes into a project update.
- Trigger: a recognizable request or condition, such as “summarize these notes as a weekly project update.”
- Input: the material Codex must receive or locate, such as notes and a project name.
- Success check: observable requirements, such as the update includes decisions, owners, and next steps, and does not invent missing details.
OpenAI’s guidance emphasizes a focused user goal. The skill’s name and description are important signals for deciding when it should be considered, so make both specific rather than broad or ambiguous. The build guide explains skill structure; OpenAI’s evaluation article discusses systematic testing.
Write the workflow in SKILL.md
Keep the main instructions in SKILL.md: what the skill does, when it applies, the steps to follow, decisions to make, and the expected output. An editorial starter pattern—not a prescribed OpenAI form—could look like this:
---
name: weekly-project-update
description: Turn project meeting notes into a weekly update when the user asks for a status summary.
---
Use this skill when the user asks for a weekly project update from meeting notes.
1. Gather the notes and project name. If either is missing, ask for it.
2. Extract progress, decisions, blockers, owners, and next steps.
3. Draft the update using the required headings and concise language.
4. Mark details not stated in the notes as unknown; do not infer them.
5. Check that each action has an owner when one is supplied and that no unsupported facts were added.
Adapt the wording and level of detail to the task. Instructions are more useful when they identify the inputs, the sequence, important branches, and the output format. Include examples when they resolve a real ambiguity; avoid examples that merely restate the rules.
Keep supporting files purposeful
Add a reference file when the workflow needs substantial background information, such as a style guide. Use a template when a repeated output must follow a fixed structure. Include a script when an executable operation is genuinely needed and can be repeated reliably. Keep such resources tied to the skill’s job; unnecessary files add maintenance without improving the workflow. OpenAI’s build guide describes these as optional supporting resources.
Choose instruction-only or script-backed
Not every workflow needs code. OpenAI’s skill evaluation guidance presents instruction-only as the default recommendation; add executable support when it solves a concrete need rather than just making the skill seem more sophisticated.
| Approach | Choose it when | Trade-off |
|---|---|---|
| Instruction-only | The task is mainly interpretation, drafting, or applying a repeatable set of decisions. | Simple to maintain, but the result depends on following the written process. |
| Script-backed | A repeatable executable action—such as a transformation or validation—is necessary to complete the workflow. | Can make that action more consistent, but introduces code that must be maintained and whose assumptions should be checked. |
The distinction is practical, not absolute: a skill can give instructions and use a script for one well-defined step. See OpenAI’s evaluation article.
Decide whether the skill needs an MCP server
A skill is the playbook: it can explain which decisions to make, which tool sequence to follow, and what result to produce. An MCP server can make supported live information or controlled actions available, including cases involving authentication or authorization. The skill does not automatically provide those capabilities, and many workflows need no server at all if their instructions and packaged resources are sufficient. OpenAI’s skills overview, dated April 10, 2026, explains this distinction.
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| Setup | Use it when | What it provides |
|---|---|---|
| Standalone skill | The task can be completed from the supplied prompt and packaged instructions or resources. | A repeatable workflow without a connected service. |
| Skill plus MCP server | The task needs supported live data, authentication, or controlled actions through a connected service. | The skill guides the work; the server supplies the available data or actions. |
Describe clearly in the skill when a connected tool is needed and what to do if it is unavailable. Do not imply that a server can perform actions or access data unless the connected service actually supports them. The build guide also covers the relationship between skills and tools.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Test whether the skill works
Before testing, define what a good result must contain and what would make it wrong. Then try representative requests, including a request that should trigger the skill and a similar one that should not. OpenAI’s evaluation guidance describes combining deterministic checks with rubric-based grading: use fixed checks for things that can be mechanically verified and a rubric for qualities such as relevance or clarity.
- Check triggering: Try a clear matching request and a nearby request that belongs to a different task. If the skill is not selected when it should be, or is selected too broadly, refine its name and description.
- Check the process: Provide the normal inputs and confirm the steps, decision points, and required resources are handled as intended.
- Check the output: Verify required sections, fields, formatting, and any factual constraints. A rubric can assess qualities that do not have a simple pass/fail test.
- Check missing information: Leave out a required input or make a connected tool unavailable. Confirm the skill asks for what is missing or reports the limitation instead of guessing.
- Revise and retest: When a test exposes a failure, improve the relevant instruction, description, resource, or script, then rerun the same case to ensure the fix holds.
Keep test cases close to the task’s real inputs. Passing one ideal example is not enough to show that a skill handles ambiguous requests, missing details, or nearby tasks appropriately.
Where you can use and share a skill
OpenAI says skills created in the Codex app can be used in the app, CLI, or IDE extension, and that repository-checked-in skills can be shared with a team; see the Codex app announcement. Product-specific setup and availability can differ. Separately, the API skills guide describes local-execution and hosted container-based forms for API use; those API details should not be assumed to describe every Codex surface.
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