skillify

Standardizing agent capability development involves applying a multi-phase audit and cross-modal evaluation process to transform raw features into robust, tested units of logic. This ensures every skill includes essential documentation, contracts, and lifecycle phases.

Cross-modal quality assurance for LLM agents is a testing mechanism that uses three frontier models from different providers to critique and iterate on output quality before tests are written. This multi-model consensus ensures high-quality outputs and validates resolver routing.

Scaffolding modular skills requires applying a standardized framework that enforces essential documentation, contracts, and lifecycle phases. This process audits raw feature ideas and locks in behavior using unit, integration, and LLM-based tests.

Yes, you need to integrate with gbrain runtime tools to execute evaluation cycles and verify resolver routing. This integration is required to run the multi-model consensus checks and validate the agent capabilities.

Yes, you can use automated scaffolding for an untested PR summarizer. The Skill applies a multi-phase audit to refine the feature and locks in its behavior with unit, integration, and LLM-based tests before production deployment.

A key limitation of using multi-model consensus for skill validation is the dependency on integrating with gbrain runtime tools to execute evaluation cycles. The process also requires three frontier models from different providers, which may introduce external provider constraints.