skill-creator

You can automate AI skill creation by drafting concepts, capturing intents, and aligning them with evaluation criteria. The system automatically runs deterministic tests and generates improvement loops to iterate until triggering reliability reaches your defined targets.

Evaluating and benchmarking AI skills involves running controlled evals to measure triggering accuracy and robustness. This approach tracks performance data across iterations, ensuring your skills meet production reliability standards before deployment.

Iterating on AI skills uses data-driven improvement suggestions generated from evaluation results. This creates a feedback loop where you track history across iterations, continuously refining the skill until user satisfaction targets are achieved.

Yes, the skill creation and testing process relies on Python with Anthropic and PyYAML dependencies. This stack supports drafting new skills, running evaluations, and managing configuration files throughout the improvement cycle.

Automated skill creation is designed for production use, handling complex intents through deterministic testing and benchmark evaluation sets. It measures performance and generates improvement loops until reliability and user satisfaction reach defined targets.