benchmark-summary

Automating benchmark analysis for clinical trial design skills involves processing GitHub issues to evaluate performance. This skill aggregates completed runs, normalizes run metadata, and compiles a structured report for consistent evaluation over time.

Data normalization for benchmark run metadata applies standardization rules to model names, ensuring consistency in reporting. This process formats varying model identifiers into uniform values before compiling the final benchmark analysis report.

Yes, generating benchmark analysis reports requires GitHub API access to fetch and process GitHub issues. The workflow depends on the gh tool to retrieve completed run data and compile the structured evaluation document.

Identifying failure patterns in clinical trial design benchmark runs involves analyzing aggregated GitHub issues from completed runs. The skill processes this data to detect common failure trends and provides recommendations for improvement.

Yes, you can apply incremental updates to a benchmark analysis document. This functionality allows the report to grow continuously by appending new normalized run metadata and failure pattern data from recent completed runs.

Yes, this benchmark analysis skill works with gh and jq dependencies. It uses gh for GitHub API access to retrieve issues and jq to parse and normalize the run metadata before compiling the structured report.