Run and store quality benchmark results for metric detectors.
npx skills add https://github.com/kayba-ai/recursive-improve --skill benchmark-kayba-ai
Please help me install this Agent Skill. Skill: benchmark Source: https://github.com/kayba-ai/recursive-improve/tree/main/recursive_improve/data Command: npx skills add https://github.com/kayba-ai/recursive-improve --skill benchmark-kayba-ai
This Skill streamlines the process of benchmarking metric quality, allowing for efficient comparisons and insights into detector performance.
Run the benchmark with the label 'v1-baseline' to assess current metric quality.
High-intent search queries and answers about installing and using this skill.
Benchmarking metric quality involves executing metric detectors to evaluate run rate, count parity, and denominator quality, then storing results for tracking improvements and identifying trends over time.
Yes, the benchmarking tool allows you to store and compare benchmark results over time to track improvements and identify trends between previous and current runs.
Detector assessment evaluates quality metrics including detector run rate, count parity, and denominator quality to provide insights into metric detector performance.
Run a metric quality benchmark before implementing changes to ensure existing metrics perform well, and benchmark again after deploying new changes to validate improvements.
No, benchmarking metric detectors has no external dependencies, allowing you to run and store quality benchmark results directly without additional setup.
EurecaMomentProcess and validate raw simulation data for benchmark evaluations.
proffesor-for-testingGenerate and index test reporting analytics for QE metrics in CI/CD pipelines.
sourcegraphGenerate aggregate CodeScaleBench evaluation reports from Harbor runs in markdown, JSON, and CSV.
bordenetCross-validate metrics with dual methods and completeness checks before reporting.
