An Engineer’s Guide to Better AI Skills: Implementing a Testing Process to Optimize Agent…

This post introduces a methodology for empirically testing and optimizing AI agent skill invocation. It details the creation of a Bash script-based test harness that pipes prompts to agents (Pin-agent/Codex and Claude Code) and captures verbose output logs. Key metrics like Core Success Rate, Edge False Positive Rate, and Overall Accuracy are defined and calculated. Optimization techniques discussed include enhancing frontmatter descriptions, using aggressive language in prompts, and leveraging AGENTS.md files. The post quantifies the performance gains achieved through these methods, particularly for Codex, and emphasizes the importance of explicit developer intent and thorough prompting for reliable agent performance.