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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteAn AI agent skill is a reusable bundle of instructions and supporting files that teaches an agent how to carry out a workflow. Its main instruction file is commonly named SKILL.md. A well-made skill defines when to use the workflow, what steps to follow, what to produce, and when to ask for help or stop. Skills are not automatically portable between products: installation, execution, sharing, and limits depend on the platform.
What is an AI agent skill?
A skill packages repeatable workflow knowledge for an AI agent. OpenAI describes a skill as a directory that teaches ChatGPT and Codex to use tools in a repeatable workflow; Anthropic describes a directory of instructions and resources that gives an agent additional capabilities. A bundle usually has a SKILL.md file and may also contain reference documents, scripts, templates, or other assets.
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A skill is different from a general-purpose model capability: it gives the agent task-specific procedures and context. It can complement connected tools rather than replace them. As OpenAI puts it, “A skill complements your MCP server by teaching ChatGPT and Codex how to use its tools in a repeatable workflow.” OpenAI’s Build skills documentation explains the relationship.
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What goes in a SKILL.md file?
A typical skill starts with YAML frontmatter containing a name and description, followed by the workflow instructions. The frontmatter helps the system identify the skill; it is not merely decorative metadata. OpenAI says the description affects when the model considers a skill, and its evaluation guidance identifies name and description as primary signals for invocation. Make both specific enough to distinguish this workflow from adjacent tasks.
For example, “Prepare a release note from merged pull requests” is more useful than “Writing help” if the skill is meant to activate for release-note work. The description should clarify both the task and the conditions under which the skill should be selected.
The body should tell the agent how to act, not just describe the subject. Include the required input, ordered steps, tool choices, expected output, decision points, and explicit limits. State facts the agent must not infer, and specify when it should ask a question, stop, or decline. Keep frequently needed instructions in the main file; move long reference material or branch-specific detail into supporting files that the skill can consult when relevant. Add scripts only when deterministic computation or file processing is useful.
How skills are loaded: progressive disclosure
Skills can be designed so an agent starts with a short description and loads more detail only when needed. Anthropic calls this “Progressive disclosure is the core design principle” in its engineering article about Agent Skills. In the architecture described in Anthropic’s documentation, metadata is available at startup, a triggered skill’s SKILL.md body is loaded when selected, and supporting references or scripts are consulted only as needed.
Anthropic’s documentation gives approximately 100 tokens per skill for metadata and an instruction body under 5,000 tokens once triggered. These are figures for Anthropic’s described design, not universal limits, cross-platform guarantees, or a target every skill must meet. The practical lesson is to keep the entry point focused and place details where they are needed instead of loading every resource for every request.
How to create a useful skill
- Choose one repeatable workflow. Define a task with a recognizable trigger and a result that can be checked. Avoid combining unrelated jobs into a broad catch-all skill.
- Write the name and description for routing. Say what the skill does and what kind of request should activate it. Include enough specificity to separate it from neighboring skills.
- Put operational instructions in
SKILL.md. Specify inputs, steps, tool usage, output format, and decision points in the order the agent should use them. - Set boundaries explicitly. Note what the agent must not assume, what information is required, and when it should ask, stop, or decline rather than guessing.
- Separate optional detail. Put lengthy references, templates, or material used only in certain cases in supporting files, then tell the agent when to consult them. Use scripts when they provide a real benefit such as reliable data transformation, not simply to make the bundle look more complete.
- Test both intended and unintended triggers. Try representative requests that should use the skill and nearby requests that should not. Check whether the skill is selected appropriately, follows its procedure, respects its boundaries, and produces the required output. Revise unclear descriptions or conflicting instructions based on what fails.
- Review bundled code and data. Inspect scripts and assets before deployment, particularly when a skill can execute code or access files.
OpenAI’s authoring guidance covers workflow design and supporting resources; its skill evaluation article discusses systematic testing and invocation signals.
Are skills portable between Claude and ChatGPT or Codex?
Do not assume that a skill directory will work identically everywhere. A shared idea—organized instructions plus resources—does not guarantee identical installation, tool access, execution, synchronization, or governance. Check the target surface and runtime before adopting or sharing a bundle.
| Platform documentation | What it establishes | What to check for your use |
|---|---|---|
| Anthropic Claude Agent Skills | Documents custom skills for Claude Code, Claude API, and claude.ai, with surface-specific availability and sharing. Skills uploaded to these surfaces do not automatically sync. API skill use requires the code execution tool and runs in a sandboxed container without network access or runtime package installation. | Which Claude surface you use, its sharing scope, and its current execution constraints. |
| OpenAI API Skills | Documents skill directory uploads and use in API execution environments, along with validation and bundle or file limits. | Current upload format, execution environment, and applicable file and bundle limits. |
| OpenAI Build skills | Provides authoring guidance for skills used with ChatGPT and Codex, including workflow boundaries and supporting resources. | The applicable product, installation method, and tool availability for the specific workflow. |
When comparing platforms, look at where the skill runs, how it is installed, what executes its tools or scripts, who can access it, and what limits or administrative controls apply. A skill that relies on a local filesystem, a hosted sandbox, or an application-provided tool may need adaptation even if its core instructions are reusable. Platform requirements can change, so consult the live documentation for the product and runtime you intend to use.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsWhat makes a skill reliable?
Having a SKILL.md file does not establish that an agent will select or follow it correctly. A useful skill has an unambiguous trigger, a procedure that can be followed in sequence, and an output that can be inspected. Test a range of requests, including plausible near-misses, and examine both invocation and execution: did the right task activate the skill, did an unrelated task avoid it, and did the agent respect the stated limits?
- Use concrete trigger language in the name and description.
- Resolve conflicts and vague directions before deployment.
- Make ask, stop, and decline conditions explicit.
- Verify the output against the skill’s stated requirements.
- Review scripts and other bundled files as code and data.
OpenAI’s evaluation guidance emphasizes testing whether skills are invoked and used as intended. Treat a skill as a workflow to validate and maintain, not as a guarantee of correct behavior.
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