find-stale-issues

To find resolved GitHub issues automatically, you can use semantic similarity analysis to cross-reference open issues against merged pull requests and recent commits. This process generates a structured triage report categorizing issues as definitely resolved, likely resolved, or duplicates.

You can audit a GitHub repository for stale and duplicate issues by running an automated triage script that performs semantic similarity analysis on repository-level issue and pull request datasets, validating issue status against actual codebase changes to produce actionable triage reports.

Yes, you need GitHub CLI (gh) and Python3 (python3) installed locally to automate issue triage. These dependencies are required to fetch repository data and run the local semantic indexing tools that process and validate issue status against actual code state.

The best way to prune hundreds of open GitHub issues in a large project is to perform semantic issue auditing against recent codebase changes. This approach identifies which open issues have already been addressed by merged pull requests, allowing maintainers to batch close resolved or duplicate issues efficiently.

Yes, semantic similarity analysis can identify duplicate GitHub issues across a repository by comparing open issues against recent pull requests and codebase changes. The automated triage report categorizes matching issues as duplicates based on their semantic relationship to existing code state.

You should not use automated semantic issue triage when your repository lacks sufficient pull request history or recent codebase changes for semantic indexing. The tool requires local semantic indexing tools and meaningful repository-level issue and PR datasets to accurately validate issue status against actual code state.