This article presents testing methods to measure and improve AI agent skill invocation reliability using Pinterest's internal agents and Claude Code.
- •Built a Bash-based test harness that parses JSON logs to detect skill invocation with positive cases (15 skill prompts) and negative cases (5 general)
- •Baseline testing showed 73% accuracy for Codex and 62% for Claude, insufficient for production workflows requiring reliable skill usage
- •Identified optimization techniques: enhanced frontmatter descriptions with architectural context, forceful language cues, and AGENTS.md documentation
- •Combined optimizations showed greater gains for Codex than Claude, but developers must provide clear, verbose prompts for reliable skill invocation
This summary was automatically generated by AI based on the original article and may not be fully accurate.
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