professor-triage

Automated GitHub issue triage cross-references issue reports with your codebase to assess impact, priority, and resolution status. It executes a multi-phase pipeline involving codebase search, cross-analysis, and GitHub action execution to label or close issues.

Automated issue triage analyzes incoming GitHub issues against your current codebase to identify duplicates, assign priority labels, and close resolved items without human intervention. This eliminates the manual overhead of managing high-volume issue queues.

Yes, automated issue triage requires integration with GitHub CLI to execute actions like labeling and closing issues. It also adheres to sensitive-path artifact protocols to ensure secure file modifications during the workflow pipeline.

Codebase-driven cross-analysis compares GitHub issue reports against actual code to determine if a bug is already resolved or requires immediate attention. This automated impact assessment prevents maintainers from reopening fixed defects.

Automated issue management workflows rely on accurate codebase search and cross-analysis to function correctly. Limitations include dependency on GitHub CLI integration and the necessity of adhering to sensitive-path artifact protocols for secure file modifications.

Automated issue triage performs priority assessment by executing a multi-phase pipeline that gathers issue data, analyzes it against the codebase, and executes GitHub actions to assign appropriate priority labels based on impact analysis.