Turn a coding request into a bounded, reviewable task brief. Enter the outcome, useful repository context, file or system boundaries, acceptance checks, and verification steps. The builder formats those details for an agent or issue; it does not decide whether the task is well designed.
Build a task brief
What belongs in a coding-agent task brief?
Start with the end state and its purpose. Add only context that helps locate or reproduce the work, then name scope boundaries and observable acceptance criteria. List the checks that would show the change is complete. The source material linked below uses closely related fields: OpenAI's Codex guidance describes outcome, verification, constraints, and boundaries; GitHub's community example for Copilot issues includes a summary, acceptance criteria, scope, constraints, and optional sub-issues.
When should a large task be split?
Use the optional ordered steps for a small sequence that still belongs in one review. If parts can be implemented and reviewed independently, turn them into separate issues or tasks with their own outcome and acceptance checks. A public GitHub community discussion recommends smaller sub-issues for complex work, while a Reddit discussion includes one user's practice of turning a breakdown into actionable GitHub issues. Those are examples of workflows, not evidence that splitting improves results in every project.
How do I keep the brief actionable?
- State behavior the reviewer can observe; leave implementation choices open unless a real constraint requires one.
- Use repository paths, issue links, and reproduction details only when you have checked that they are correct.
- Write boundaries as concrete paths, interfaces, or decisions. Say what needs approval if the work crosses them.
- Make acceptance criteria independently checkable and avoid promising a quality improvement from the wording alone.
- Before handing off, confirm the brief matches the repository and decide which checks a human must review.
Frequently asked questions
Does the builder split work into separate issues automatically?
No. It formats the details you provide and includes an optional ordered-steps section. Decide yourself whether the steps are independent enough to become separate tasks.
Does a more detailed brief guarantee a better coding result?
No. This is a formatting aid, not a benchmark or quality guarantee. Check the implementation against the criteria, inspect the diff, and run the relevant project checks.
Does this page send my brief to an agent?
No. The text is assembled in your browser and is not submitted to a service. Copy it into your issue tracker or coding agent only after reviewing it and removing any sensitive information.
What evidence supports the fields?
OpenAI's Codex guide describes outcomes, verification, constraints, and boundaries for a scoped objective. A GitHub community discussion shares an agent issue template with acceptance criteria and scope fields; a Reddit thread has an individual example of turning a task breakdown into GitHub issues. These sources document guidance and individual practices, not representative demand or proven outcome gains.
Sources · checked November 3, 2026
- OpenAI Developers: Using Goals in Codex · outcome, verification, constraints, and boundaries
- GitHub Community discussion #174930: Getting better results from Copilot Coding Agent · example task template and sub-issues
- Reddit r/codex: How do you prompt your coding agent? · individual comment describing breakdown into GitHub issues
Related: choose a coding-agent workflow, choose a GitHub Copilot agent workflow, and check a Copilot instructions template.
Methodology and limitations
This browser-only form formats fields the reader supplies into a Markdown task brief. It does not split work automatically, assess task quality, contact an agent, or send field contents to a service. The field structure is based on current OpenAI Codex guidance and a GitHub community example. Public GitHub and Reddit discussions are cited as qualitative examples of task-writing and breakdown practices, not representative demand or evidence of improved outcomes.
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