skillify

To turn raw feature ideas into testable skills, you can use a scaffolding process that generates standard frontmatter, execution contracts, and phase gates. This structures scattered ideas into fully defined, traceable skill units for AI agents.

Cross-modal eval gating is a quality assurance mechanism that enforces routing and validation checks across different evaluation modes. It ensures that generated skills meet repeatable quality standards before they proceed to the next lifecycle phase.

Standardizing AI skill creation reduces feature drift by applying deterministic scripts, resolver triggers, and structured phase gates. This enforces a repeatable skill lifecycle, ensuring all generated skills maintain consistent quality and traceable outputs.

Yes, skill testing scaffolding can be wired into CI pipelines. The scaffolding process extracts deterministic scripts and generates a starter script skeleton, allowing automated validation and testing to run directly within your continuous integration workflows.

The best way to scaffold a feature into a deployable skill is running a scaffold command that generates a SKILL.md file alongside starter script skeletons. This applies frontmatter and contracts to standardize the skill lifecycle from creation to deployment.

Resolver triggers are enforced during skill deployment to ensure proper routing and execution. They are integrated with cross-modal eval gating to validate that the skill unit functions deterministically before it is approved for deployment.