What problem does it solve?
Creating effective AI agent skills requires more than just writing instructions — it demands systematic testing, quantitative evaluation, and iterative refinement to ensure reliable triggering and consistent performance. Without a structured workflow, skills often undertrigger, behave inconsistently, or fail to handle edge cases.
Core Features & Use Cases
- Structured Skill Creation: Guides you through capturing intent, drafting SKILL.md files with proper progressive disclosure, and organizing bundled resources like scripts and references.
- Quantitative Evaluation: Runs parallel test cases with baseline comparisons, grades assertions automatically, and aggregates benchmark statistics with variance analysis.
- Description Optimization: Uses machine learning to iteratively improve skill descriptions for better trigger accuracy, with train/test splits to prevent overfitting.
- Blind Comparison: Provides rigorous A/B testing of skill versions using independent agents to judge output quality without bias.
Quick Start
Use the skill-creator skill to design a new skill for your specific task, run evaluation prompts against it, and automatically refine its description until it triggers reliably.