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A useful first agent skill could turn a recurring task—such as converting meeting notes into a concise action list—into a repeatable workflow with clear inputs and a predictable output.
What an agent skill is
An agent skill is a reusable bundle of instructions and supporting files, not just a prompt pasted into a chat. Its central file is SKILL.md: a manifest that describes the skill and gives the agent its instructions. OpenAI’s Skills guide describes the directory-based structure and how skills are made available in supported runtimes.
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For a first skill, choose one repeated task with a recognizable starting point and result. For example, a meeting-notes skill could take rough notes as input and produce decisions, action items, owners, and deadlines. Keep it narrow enough that you can explain when it applies and what a successful result looks like.
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Create the minimum skill bundle
Start with a directory containing one file:
meeting-action-list/
└── SKILL.mdUse YAML front matter at the beginning of SKILL.md, followed by Markdown instructions. A minimal example:
#1 Best Overall
---
name: meeting-action-list
description: Turn meeting notes into a concise list of decisions and action items. Use when the user provides meeting notes and wants follow-up work organized.
---
# Meeting action list
## Inputs
- Meeting notes, in any readable format.
## Instructions
1. Identify decisions and proposed actions separately.
2. For each action, include an owner and deadline only when the notes state them.
3. Do not invent missing owners, dates, or decisions. Mark missing details as unspecified.
4. Keep the wording concise and preserve important context.
## Output
Return these sections:
- Decisions
- Action items, with owner and deadline where available
- Open questionsThis is an example structure, not a claim that every runtime requires these exact headings. Follow the format requirements of the platform where you plan to use the skill. OpenAI’s guide says its front-matter validation follows the Agent Skills specification; consult the guide for current requirements and runtime-specific setup.
Write instructions an agent can follow
Make the skill’s purpose and operating procedure explicit. In Codex, the skill’s name and description are important signals for deciding whether it should be invoked; the description should tell the agent what the skill does and when it applies. OpenAI’s guidance on testing agent skills illustrates why focused, clear metadata matters.
- Define when to use it. Describe the user goal or type of task that should trigger the skill. Avoid broad descriptions such as “help with work.”
- Specify the input. Say what information or files the workflow expects, and how to handle incomplete information.
- Give ordered actions. Break the work into concrete steps. State constraints such as preserving facts, separating categories, or asking for missing information when necessary.
- Describe the output. Name the expected format and the details it should contain. This makes it easier to judge whether the result meets the user’s goal.
These are practical authoring choices, not a universal mandatory template. The useful test is whether someone unfamiliar with your intention can follow the instructions and recognize the expected result.
Add supporting files only when they help
A single SKILL.md is enough to begin. Add files when they make the workflow clearer or more repeatable: a reference document for rules the agent needs to consult, a template for a recurring output, a script for a deterministic operation, or an asset used by the task. OpenAI’s skill-building guidance describes these as optional supporting materials.
Keep the main workflow in SKILL.md and point to supporting files from the instructions when needed. Avoid adding material that does not change how the task is completed; unnecessary files make the bundle harder to understand and maintain.
Make the skill available in its intended runtime
There is no single installation step for every agent environment. The deployment route depends on where the skill will run:
Rank #4
| Route | How the skill becomes available | What to do |
|---|---|---|
| Local or sandbox directory discovery | The runtime discovers skills from capability directories configured for that environment. | Place the skill directory in a configured location and follow that runtime’s setup instructions. |
| API-hosted skill | The API guide documents uploading a directory or a ZIP bundle. | Package the skill files using the upload method supported by the API workflow you are implementing. |
OpenAI documents both directory and ZIP upload, as well as capability-directory discovery for Agents API sandboxes, in its Skills guide. These are different availability mechanisms: uploading a bundle for an API-hosted skill is not the same as making a directory visible to a sandbox. Check the documentation for the exact runtime before choosing a location or upload method.
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- Purpose: The name and description identify one focused task and when it applies.
- Instructions: The steps specify inputs, actions, and the expected result without relying on unstated assumptions.
- Files: Supporting resources have a clear purpose and are referenced where the workflow needs them.
- Availability: The directory or uploaded package is set up for the runtime you intend to use.
Start with the smallest bundle that can perform the workflow. Add detail or supporting files only when a real use case shows what is missing.
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