Use a prompt to define what you want GPT-6 Astra to do now; use a skill to preserve instructions for a workflow you expect to repeat. The two work together: put stable procedures in a focused skill and keep changing task details—such as inputs, deadlines, repository state and requested output—in the prompt.
What is the difference between a prompt and a skill?
A prompt steers the task at hand. It should give Astra the goal, relevant context, constraints and a clear description of a good result. For coding, OpenAI recommends defining the model’s role, how it should use tools and the standards the output must meet. See the OpenAI prompting guidance.
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A skill packages instructions for a repeatable workflow or a particular sequence of tool use. A useful skill describes the inputs it expects, the steps to follow, the outputs to produce, its limits and any supporting resources. It teaches a workflow; it does not itself provide a tool’s live data or permissions. OpenAI distinguishes these roles in its skills guide.
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| Factor | Prompt | Skill |
|---|---|---|
| Scope | The specific task or request in front of you. | A recurring workflow or tool sequence. |
| What belongs there | Current facts, files or inputs, deadlines, constraints and the requested result. | Stable procedures, recurring checks, output conventions and workflow-specific limits. |
| When it changes | Update it as the task context changes. | Review it when the repeatable process or its assumptions change. |
| Selection and context | Directly attached to the request. | Astra must recognize that the skill applies; its instructions also use context. Clear, concise triggers help. |
This distinction is a practical rule of thumb drawn from OpenAI’s descriptions, not a claim that one method is inherently more capable. No published controlled comparison of prompt-only and skill-based performance was identified in the cited sources.
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When should you keep an instruction in the prompt?
Keep it in the current prompt if it is specific to this task, likely to change next time, or needed to explain what success means now. For example, a request to review a particular repository should name the relevant files or state, the issue to investigate, any constraints and the format of the answer. Do not make a skill out of a one-off deadline or a temporary instruction.
For code tasks, make the requested role, tool-use expectations and output standards explicit rather than assuming Astra will infer them. If an instruction applies only to one request, placing it in the prompt also avoids loading it into unrelated work.
When is a skill worth creating?
Create a skill when you expect to use the same procedure across multiple tasks and want its important steps to remain consistent. Give it a narrow description that says when the workflow applies. A description that tries to cover too many situations can cause irrelevant guidance to load; when many skills compete, long descriptions may also be shortened. OpenAI’s September 11, 2026 guidance on skills recommends concise triggers and progressive disclosure.
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How to structure a skill with multiple workflows
Do not put every branch and exception into one oversized root file. Keep the root SKILL.md short: describe the skill’s purpose and trigger, then direct Astra to supporting material only when a particular workflow needs it. Put conditional instructions, references, assets or scripts in separate files. This progressive-disclosure approach keeps the default guidance focused while making extra detail available when relevant.
OpenAI calls progressive disclosure a key marker of a useful skill. Its guidance also cautions against over-elaborate itineraries: rigid recipes can get in the way when a model can handle nuance. Make steps exact where consistency or safety requires them, but leave room for judgment elsewhere.
A practical decision rule
- Ask whether the same procedure will recur. If this is a one-time request, state the instruction in the prompt.
- If it will recur, make it a focused skill. Write a clear trigger and include the stable procedure, not the details of today’s task.
- Keep changing context in the prompt. Provide current inputs, deadlines, repository state, constraints and output requests each time.
- If the skill branches, use a short router. Point from the root file to the relevant supporting instructions instead of loading every workflow at once.
- Review the full instruction environment. Check skills and repository files such as
AGENTS.mdfor contradictions, obsolete rules or instructions that do not apply to the current task.
Why review skills and repository instructions for Astra?
OpenAI’s GPT-6 guide says Astra can be more sensitive to instructions in skills and repository-level files such as AGENTS.md. Review those instructions periodically, especially if Astra pauses or follows a rule that seems unrelated to the task. The GPT-6 guide supports auditing the instruction environment; it does not mean that every skill or repository instruction should be removed.
OpenAI has reported a 72.6% result at roughly 40 minutes per task for GPT-6 Astra, compared with 65.7% at roughly 75 minutes for GPT-5.6 Sol, in latency simulations on OSWorld 2.0. These are vendor-reported computer-use evaluation figures, not a test of prompting versus skills, so they do not show which instruction format performs better.
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Can you use a prompt and a skill together?
Yes. Use the skill for the stable workflow and the prompt for the task-specific request. For example, a skill might describe the recurring steps for reviewing code changes, while each prompt identifies the change to inspect, current repository context, constraints and desired report. That separation makes a repeatable process reusable without freezing details that change from one task to another.
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