skillify

Name: skillify
Availability: InStock
Author: starlink-awaken

Standardize agent skill development through multi-phase audits and cross-modal evaluation.

Updated Jun 10, 2026

Search Tags:#automation #scaffolding #meta-skill #quality-assurance #agent-development #llm-evals

Ready to run

npx skills add https://github.com/starlink-awaken/omostation-gbrain --skill skillify-starlink-awaken

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: skillify
Source: https://github.com/starlink-awaken/omostation-gbrain/tree/main/skills/skillify
Command: npx skills add https://github.com/starlink-awaken/omostation-gbrain --skill skillify-starlink-awaken

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill solves the problem of inconsistent, untested, and unverified agent capabilities by providing a standardized framework to turn raw ideas into production-ready, reliable units of logic.

Core Features & Use Cases

Standardized Scaffolding: Ensures every skill includes essential documentation, contracts, and lifecycle phases.
Cross-Modal Quality Gate: Uses three frontier models from different providers to critique and iterate on output quality before tests are written.
Use Case: When you have a new feature idea like a PR summarizer, use this Skill to audit, refine, and lock in its behavior with unit, integration, and LLM-based tests.

Quick Start

Use the skillify skill to audit and upgrade the current feature into a properly skilled unit.

Frequently Asked Questions about skillify

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

FAQPage Schema

How do I standardize agent capability development for new features?▼

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.

What is cross-modal quality assurance for LLM agents?▼

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.

How do I scaffold modular skills to include lifecycle management and documentation?▼

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.

Do I need gbrain runtime tools to execute LLM evaluation cycles?▼

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.

Can I use this automated scaffolding for an untested PR summarizer feature?▼

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.

What are the limitations of using multi-model consensus for skill validation?▼

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.