moai-workflow-research

Set up component optimization by targeting a specific skill's SKILL.md to initiate an eval suite with predefined parameters. You define experiment configurations in YAML, and the workflow handles baseline comparisons, structured data collection, and change tracking automatically.

Binary evaluation in automated research workflows systematically tests component changes against a baseline to determine performance improvements. It uses structured data collection and safety layers to ensure reliable, iterative optimization of skills and agents.

Yes, you can use YAML to manage experiments for agent performance testing. YAML-defined experiment setups facilitate automated evaluation suite creation, enabling researchers to systematically refine auto-research patterns and track iterative changes.

Yes, component experimentation works for optimizing skills, rules, and configurations. The workflow conducts structured experiments across all moai-adk components, applying change logging and baseline comparisons to drive iterative improvements.

The main limitation of automated evaluation loops is reliance on predefined parameters and binary outcomes, which may not capture nuanced performance shifts. Effective self-improvement requires carefully structured YAML setups and consistent baseline data to avoid skewed optimization results.