gh-issue

Generating GitHub issues from bug reports involves using an LLM to translate ambiguous bugs into structured narratives. This process applies a consistent template covering the problem, impact, proposed solution, and acceptance criteria to improve triage speed.

The mandatory llm label ensures generated GitHub issues appear in LLM-facing feeds and dashboards. Applying this label during issue creation enforces consistent tracking and visibility for automated workflows across product and engineering projects.

Yes, you can use a structured template for feature requests and improvements. This enforces uniform communication by requiring sections for problem, impact, solution, and acceptance criteria across all project issues.

Yes, acceptance criteria are required to create actionable GitHub issues. Defining clear criteria ensures that the proposed solution is testable and that the issue narrative remains consistent for triage and implementation.

The best way to automate issue narratives for LLM workflows is to apply a structured template that translates ambiguous bugs into clear text. This enforces guidelines for problem, impact, solution, and acceptance criteria generation.

Using an LLM to generate GitHub issue templates solves the problem of unclear bug reports. It translates ambiguous inputs into actionable issues with structured problem, impact, and solution narratives, improving triage speed and consistency.