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What a skill can—and cannot—do on its own
OpenAI describes skills as reusable instructions and resources that help an AI system follow a workflow. In the plugin model, a skill teaches the workflow, while an MCP server supplies live information, authentication, authorization, and controlled actions. The distinction matters: instructions can shape what the model tries to do, but they do not, by themselves, establish what data or actions are permitted.
A useful way to assess a skill is to separate three parts:
- The skill: tells the model how to approach a task.
- The tool or integration: connects the workflow to a data source or action.
- Permissions and execution environment: determine what the tool or agent can actually access or change.
This is a practical model, not a guarantee that every AI product implements controls in the same way. See OpenAI’s API skills guide and its plugin concepts guide.
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Can a skill access private files or connected services?
It depends on the product and configuration. A skill may influence the model’s plan or use of a tool, but the available integrations, credentials, access grants, network rules, sandbox, and user or administrator permissions determine whether private information is reachable. Neither “skills can access all your files” nor “skills can never access private data” is a reliable universal rule.
The available product documentation cannot establish what a particular installed skill can reach in your account. Check the integrations and credentials actually enabled for that workflow, along with the applicable workspace and execution settings. A connection to an external service may also mean that service’s own storage and data-processing terms apply.
Can a skill run actions or change data?
A skill can guide an AI system to use an available action-capable tool, but the skill itself is not the authorization for that action. What the system can do depends on the connected tool, its authentication and permissions, and any execution boundaries or approval requirements. If a workflow can write data or take a high-impact action, OpenAI’s API guidance says to require explicit approval before execution. Read the skills guide.
For a specific workflow, identify the actions its integrations expose and whether the system can execute them automatically, must request approval, or is blocked by policy. Do not assume that a skill’s description alone accurately tells you the effective permissions.
Why should you review a skill before using it?
Instructions can influence planning and tool use. OpenAI warns that unvetted automation can create risks including prompt injection, data exfiltration, and destructive actions when the necessary tools and permissions are available. Review both the skill and its supporting files before enabling it, especially when it comes from an external source. A scan can help, but it is not a substitute for review or organizational policy.
- Check what the skill instructs the model to do and which services or tools it expects to use.
- Limit the workflow to the integrations and permissions it needs.
- Use explicit approval for writes or high-impact actions.
- Review available logs or records after use, where the product or workspace provides them.
OpenAI’s guidance on review and security risks is in the API skills documentation.
How do sandboxing and approval differ?
Sandboxing defines execution boundaries, such as which paths an agent may write to and whether it can access the network. Approval policy governs whether the system must ask before crossing a boundary or performing a sensitive operation. One control limits what is possible; the other controls when a permitted operation requires human authorization. The exact behavior depends on the product and configuration.
OpenAI’s article about running Codex safely describes controls in a particular managed enterprise deployment, including sandbox and approval policies, constrained network destinations, secure OS keyring storage for CLI and MCP OAuth credentials, and workspace-pinned login. These are described deployment controls, not a statement of default settings for every Codex user or product.
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What should users and administrators check?
- Review the skill and its files. Understand the instructions and supporting resources before enabling a skill from an outside source.
- Restrict distribution and use. In ChatGPT, workspace administrators have controls for who can create, install, share, or use workspace-managed skills. Check the settings available in your own product surface.
- Minimize connected access. Give the workflow only the integrations and permissions required for its task; the skill does not replace a server’s authentication and authorization responsibilities.
- Set execution boundaries. For Codex, configure sandbox and network policy for the intended task rather than assuming a particular default.
- Gate consequential actions. Require explicit approval for writes or high-impact actions, and use available logs or records to review activity.
- Check connected-service terms. A third-party service invoked by a skill may have separate storage and processing policies.
ChatGPT skill availability and management differ by product and workspace settings. OpenAI’s Skills in ChatGPT help article says skills are available to eligible Business, Enterprise, Healthcare, and Edu users subject to workspace settings and product availability; installation and syncing can differ across products, and Codex may be governed separately.
How does using a skill affect privacy?
Privacy depends on the product, plan, workspace settings, and any external services the skill uses. OpenAI says data shared with a skill is not used to improve models by default for ChatGPT business plans. That statement should not be generalized to every plan or product, or to third-party services: external services and resources used by a skill can have their own storage and processing terms. Consult the ChatGPT Skills help article and the terms for any connected service.
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