What are good acceptance criteria for AI coding?
Good acceptance criteria are observable, testable, and tied to user behavior. They tell the agent what done means and tell the reviewer what to inspect.
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Markdown
An acceptance criteria generator turns a feature idea into observable conditions for accepting generated code. It helps developers define expected behavior, edge cases, and review checks before implementation starts, so an AI coding loop has a clearer target than a vague natural-language request.
Enter a feature idea, user role, application type, expected success behavior, and likely failure modes. The tool returns criteria in a format that can be pasted into a PRD, issue, or story.
Agent loops are easier to review when each story has a clear definition of done. Acceptance criteria keep the work grounded in observable behavior rather than vague intent.
They reduce arguments with the agent after the fact. Instead of asking whether a change “looks right,” the reviewer can check whether the expected behavior, edge cases, and failure states were handled.
Generate criteria first, add them to a PRD or task-splitting workflow, then run implementation only after the story is small enough to review.
Use this tool before a coding agent edits files. Then move from tool output into PRD-driven planning, a first Hal loop, or a review-before-merge checklist. Treat generated text as a draft and verify it against the repository before use.
Before adopting Hal, inspect the source repository, install guide, pricing page, and machine-readable pricing. This tool does not guarantee code quality, test success, or merge safety.
Short answers before you hand the output to an agent workflow.
Good acceptance criteria are observable, testable, and tied to user behavior. They tell the agent what done means and tell the reviewer what to inspect.
No. It produces a checklist for implementation and review. Developers still need to test and inspect the actual changes.
Yes. Copy the generated criteria into a PRD or use the PRD generator to produce a fuller markdown document.
Prepare better PRDs, stories, and standards before an agent touches the repo.
generator
Generate a markdown PRD, user stories, acceptance criteria, and a Hal-ready planning prompt from a feature idea.
analyzer
Check whether a product requirement is specific enough for an AI coding loop before an agent touches the repo.
analyzer
Break a feature request into reviewable implementation stories and score whether it is safe for an AI coding loop.
Next step
Hal is a terminal-first CLI for PRD-native coding loops. Use the generated markdown as planning input, keep the work bounded, and review agent output before merging.