Show HN: Agent-skills-eval – Test whether Agent Skills improve outputs
Agent-skills-eval is a testing framework that empirically measures whether AI agent skills (domain knowledge modules) actually improve model outputs through controlled A/B testing with baseline comparisons and judge-model grading. For IT leaders, this addresses a critical gap in AI deployment: moving from assumption-based capability claims to measurable, evidence-backed validation of agent enhancements. This tool enables organizations to govern and optimize their AI agent implementations with rigor, reducing waste on ineffective skills and building confidence in production deployments through reproducible evaluation artifacts.
Hacker News3 min read
Agent-skills-eval is a testing framework that empirically measures whether AI agent skills (domain knowledge modules) actually improve model outputs through controlled A/B testing with baseline comparisons and judge-model grading. For IT leaders, this addresses a critical gap in AI deployment: moving from assumption-based capability claims to measurable, evidence-backed validation of agent enhancements. This tool enables organizations to govern and optimize their AI agent implementations with rigor, reducing waste on ineffective skills and building confidence in production deployments through reproducible evaluation artifacts.