Translate benchmark outcome and summary JSON files into human-readable performance reports.
npx skills add https://github.com/surus-lat/benchy --skill read-results
Please help me install this Agent Skill. Skill: read-results Source: https://github.com/surus-lat/benchy/tree/main/.agent/skills/read-results Command: npx skills add https://github.com/surus-lat/benchy --skill read-results
This skill solves the communication gap between complex AI benchmarking data and stakeholders who need actionable insights without wading through raw JSON files or technical logs.
Ask the read-results skill to summarize the latest benchmark run found in the outputs directory.
High-intent search queries and answers about installing and using this skill.
To translate benchmark JSON files into a performance report, you need a tool that parses task-specific metrics and sample-level predictions to generate plain-English status updates. This process identifies failure patterns and highlights areas for model improvement.
Analyzing AI evaluation results for model optimization involves examining predefined performance thresholds and error types within your benchmark data. This approach provides actionable guidance such as prompt adjustments or data validation steps to improve outcomes.
Identifying failure patterns from sample-level benchmark predictions requires analyzing worst-performing samples within your outcome summaries. This highlights specific areas for improvement and translates technical metrics into actionable insights for model optimization.
Yes, you can generate plain-English status updates from raw benchmark outcome files by processing the JSON data through a summarization tool. This bridges the communication gap between complex AI benchmarking data and stakeholders needing actionable insights.
AI performance metrics analysis provides actionable recommendations like prompt adjustments and data validation based on predefined thresholds and error types. These concrete next steps are derived from identifying worst-performing samples and failure patterns in the benchmark data.
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