backlog-refinement

Preparing GitHub issues for autonomous agents requires enforcing strict readiness criteria and automated body rewriting to ensure tasks are bounded, verifiable, and self-contained. This skill assesses open issues against a versioned rubric to curate the ready queue.

Autonomous agents fail on messy GitHub issues because they lack clear acceptance criteria, file pointers, and out-of-scope guardrails. Automated backlog refinement rewrites issue bodies to enforce these strict requirements, preventing task bails caused by ambiguous scope.

Assessing GitHub backlog readiness involves evaluating open issues against a centralized rubric to verify they are bounded and self-contained. This skill uses GitHub CLI integration to perform label management and disqualify issues that lack clarity for autonomous execution.

Yes, you can prevent recurring agent bails through closed-loop learning. This skill aggregates post-run failures to sharpen the readiness rubric and disqualifiers, ensuring the same types of ambiguous issues are blocked from the agent execution loop.

Yes, GitHub CLI integration is required to automate backlog refinement. The CLI enables the skill to perform automated issue assessment, label management, and body mutation directly on your GitHub repository to prepare tasks for agents.

The best way to rewrite GitHub issue bodies for coding agents is applying an automated refinement process that injects clear acceptance criteria, file pointers, and out-of-scope guardrails based on a centralized rubric, ensuring tasks are completely self-contained.