Skill Developer

To automate skill development for LLM workflows, you can draft, evaluate, and improve prompts through an integrated CLI. This streamlines the lifecycle by running comprehensive evaluations and applying automated improvement suggestions based on critic feedback.

The best way to evaluate LLM skills across multiple configurations is by running comprehensive evaluation modes via CLI. These modes test performance, log evaluation history, and generate critic feedback to suggest targeted improvements.

To draft and promote an LLM skill to production, use CLI commands to initialize the draft, run evaluations, apply improvements, and finally move the validated skill to production categories.

LLM evaluation timeouts and connection failures often stem from network instability or API configuration issues. Troubleshooting these common problems involves checking LLM connection parameters and ensuring timeout limits accommodate workflow complexity.