sglang-bisect-ci-regression

To bisect CI regressions in SGLang, the Skill extracts failure signatures from scheduled or rerun workflows and performs temporal bisection across the passing and failing commit window. It uses GitHub CLI and git history to pinpoint the regression commit range.

The best way to investigate recurring GitHub Actions failures is extracting failure signatures like error messages and affected tests, then correlating runner and hardware details. This approach identifies environment differences and regression introductions for fast triage.

Yes, you can reproduce CI test failures on remote GPU hosts using SSH. The Skill supports an optional remote reproduction phase to validate failures and environment differences outside the original GitHub Actions runner.

Yes, the bisection workflow supports both main-branch scheduled CI runs and PR-triggered runs. It lists commits and applies temporal bisection across these workflows to accurately identify the regression introduction window.

You extract failure signatures from CI runs by analyzing error messages, affected tests, and involved PRs. The Skill automates this extraction phase from rerun workflows to build a structured report summarizing timeline and evidence.

For intermittent SGLang CI failures, you should correlate runner and hardware details to find environment differences. The Skill's runner correlation phase isolates whether the regression stems from code changes or specific runner configurations.