skillify

Turns feature ideas into testable AI agent skill units with frontmatter and contracts.

Updated May 16, 2026

Search Tags:#testing #scaffolding #quality-gate #frontmatter #resolver #cross-modal #skillify

Ready to run

npx skills add https://github.com/Morris-UTRUST/GBrain-knowledge --skill skillify-morris-utrust

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: skillify
Source: https://github.com/Morris-UTRUST/GBrain-knowledge/tree/main/skills/skillify
Command: npx skills add https://github.com/Morris-UTRUST/GBrain-knowledge --skill skillify-morris-utrust

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Skillify provides a structured approach to turning scattered feature ideas into fully defined, tested, and traceable skills for AI agents, reducing drift and ensuring repeatable quality.

Core Features & Use Cases

Meta-skill scaffolding: frontmatter + contract + phases + outputs.
Cross-modal eval gating and resolver integration to ensure quality and routing.
Script extraction and testing scaffolding that can be wired into CI.

Quick Start

Run the skillify scaffold on a feature to generate a SKILL.md and starter script skeleton.

Frequently Asked Questions about skillify

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I turn raw feature ideas into testable skills for AI agents?▼

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.

What is cross-modal eval gating in skill validation?▼

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.

How do I standardize AI skill creation and reduce feature drift?▼

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.

Can I wire skill testing scaffolding into my CI pipelines?▼

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.

What is the best way to scaffold a feature into a deployable skill?▼

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.

Do I need resolver triggers for AI agent skill 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.