Parse run_outcome.json to diagnose AI benchmark failures and metrics.
npx skills add https://github.com/surus-lat/benchy --skill interpret-run
Please help me install this Agent Skill. Skill: interpret-run Source: https://github.com/surus-lat/benchy/tree/main/.agent/skills/interpret-run Command: npx skills add https://github.com/surus-lat/benchy --skill interpret-run
This skill resolves the complexity of interpreting raw benchmarking outputs by providing a structured, reliable method to parse JSON artifacts and diagnose performance failures in AI systems.
Use the interpret-run skill to analyze the benchmark results located in the outputs directory for the latest model run.
High-intent search queries and answers about installing and using this skill.
To diagnose AI benchmark results, you parse JSON output files like run_outcome.json to identify failure points, error rates, and status codes. This process reveals whether degraded performance stems from network connectivity, prompt formatting, or model capability mismatches.
Parsing run_outcome.json systematically allows you to pinpoint specific evaluation failures by reviewing task-specific metrics, extraction quality scores, and status codes. Automated diagnosis identifies invalid response patterns and connectivity issues across complex evaluation pipelines.
Degraded performance in an AI model evaluation run can stem from network connectivity issues, prompt formatting errors, or model capability mismatches. Diagnosing benchmark JSON artifacts helps isolate the specific failure mode causing the degradation.
Yes, you can aggregate task-specific metrics and error rates from benchmark JSON artifacts. Metric aggregation summarizes performance data and extraction quality scores into actionable insights for complex evaluation pipelines.
Automated benchmark diagnosis works with complex evaluation pipelines by systematically reviewing task-specific metrics, error rates, and status codes. It requires structured access to run_outcome.json and associated metric files to function properly.
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