OpenAI GPT-6 Astra Prompting and Configuration Guide

One-sentence conclusion

This official guide provides reusable prompts for Astra's initiative, instruction priority, writing style, subagent delegation, and testing calibration, together with Responses API migration requirements.

Applicable scenarios

• Suitable tasks: Long-running agents, browser and coding tool use, multi-agent orchestration, and professional work requiring stable output structure.

• Unsuitable tasks: Legacy configurations that require none reasoning or depend on temperature, top_p, or top_logprobs.

• Applicable model version: gpt-6-astra.

• Applicable client, agent, or API: Responses API; Codex or a custom multi-agent harness. Astra supports Chat Completions too, but tool calling requires Responses API.

• Recommended reasoning effort and parameters: Start at low and adjust against acceptance tests; Astra does not support none. Remove temperature, top_p, and top_logprobs (and logprobs for Chat Completions) and use reasoning.effort or reasoning_effort.

Directly usable content

The following are the page's published prompt excerpts, grouped into reusable modules.

Initiative and follow-through

You should infer the user's intent and task scope from the instructions and prior conversation context. Your job is to bias towards action and carry the user's intended task to completion.

When the user expresses intent to perform new work or fix an existing issue, persist until the user's intended goal is complete. Progress autonomously towards the user's goal unless they are clearly destructive or irreversible.

Before asking the user clarifying questions, you should complete the work that is already authorized from context and necessary to make the proposed action concrete and reviewable. The user should be approving a concrete, reviewable result.

Instruction priority and transparency

The user's instructions take precedence over guidelines provided in a skill. If explicit user instructions conflict with a skill's instructions, prioritize the user's instructions.

If a skill causes you to ask for permission or confirmation, pause, leave requested work unfinished, or diverge from the user's intent, name and link to the exact SKILL.md file you read, quote the relevant instruction, and briefly explain how it applies.

Concise writing and technical communication

Default to using clear, concise paragraphs, each developing one main idea. Use lists only when the information is genuinely parallel, sequential, or easier to compare. Use plain, simple language: familiar words, concrete examples, and precise verbs. Prefer active voice and direct statements.

Use plain language over jargon, and reference technical details only to the degree that it helps illustrate an idea or your work to the user. Calibrate the explanation to the level of background knowledge assumed from the user's prompt and context.

Subagent delegation

If at any point you can parallelize work by delegating tasks to another agent, you should do so using collaboration tools if it could save time or improve quality.

Testing and verification calibration

Do not write tests for reversible, low-impact changes that mirror the implementation. If you do choose to verify your work with tests, make sure that the tests are meaningful and necessary to verify implementation.

Run tests appropriate to the change and complete required checks. Once those pass, broaden or repeat testing only when new changes, failures, or unresolved concerns justify it; otherwise, continue toward completing the task.

API configuration

Set model to gpt-6-astra. If you currently use none or minimal reasoning effort, start with low and compare results. Otherwise, preserve your current effective reasoning effort.

Use the Responses API for tool calling. Remove temperature, top_p, and top_logprobs. For Chat Completions, also remove logprobs.

Reproduction configuration and evidence boundaries

• The page explicitly recommends gpt-6-astra and starting reasoning at low; the right setting must still be compared against an application's acceptance criteria.

• The page describes asynchronous tool calls, mid-turn steering, compaction, persisted reasoning, and multi-agent orchestration; a custom harness remains responsible for executing tools and managing pending work.

• This record includes only prompts and configuration guidance visible on the source page. Unexpanded X or Reddit pages and Google snippets were not treated as source text.