Analyze AI evaluation frameworks to identify design gaps and missing tests.
npx skills add https://github.com/sorawit-w/agent-skills --skill ai-eval-review
Please help me install this Agent Skill. Skill: ai-eval-review Source: https://github.com/sorawit-w/agent-skills/tree/main/skills/ai-eval-review Command: npx skills add https://github.com/sorawit-w/agent-skills --skill ai-eval-review
This Skill identifies gaps and weaknesses in the design of AI evaluation frameworks, helping teams verify that their evaluation strategies accurately measure success and robustness.
Ask the AI to review your evaluation plan by loading your existing ai-eval-review.md file or starting fresh; it will generate a comprehensive assessment and visual report.
High-intent search queries and answers about installing and using this skill.
To evaluate robustness, you need a structured assessment of offline criteria, bias, and drift detection. This analysis reviews your evaluation plan to identify missing validation steps and untested failure modes for AI features.
Responsible AI evaluation embeds trustworthy performance metrics and compliance checks into your framework. Drift detection monitors for shifts in model behavior over time, and this analysis flags missing signals in your current setup.
Finding gaps requires a systematic review of your offline and online testing criteria. This assessment analyzes your existing eval plan to detect weak signals, missing validation steps, and untested failure modes.
Yes, you can load an existing evaluation plan file to review its design. The analysis assesses the current framework, identifies missing tests across robustness and compliance criteria, and produces a detailed report.
The best way is a comprehensive assessment that evaluates offline criteria, robustness, and drift detection together. This ensures your measurement strategy captures critical failure modes and aligns with regulatory requirements.
Yes, this review operates without external dependencies. It analyzes your evaluation framework text directly to assess design gaps, weak measurement signals, and compliance considerations for responsible AI development.
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