skillify

Name: skillify
Availability: InStock
Author: sigmacodeat

Convert raw features into structured agent skills with SKILL.md definitions and validation checks.

Updated Jun 20, 2026

Search Tags:#automation #testing #skill creation #evaluation #agent workflows #resolver

Ready to run

npx skills add https://github.com/Sigmacodeat/subsumio-web --skill skillify-sigmacodeat

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: skillify
Source: https://github.com/Sigmacodeat/subsumio-web/tree/main/server/skills/skillify
Command: npx skills add https://github.com/Sigmacodeat/subsumio-web --skill skillify-sigmacodeat

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps teams turn raw features and capabilities into structured, discoverable, and validated agent skills instead of leaving useful functionality undocumented or unresolvable.

Core Features & Use Cases

Skill Definition Auditing: Evaluates whether a feature meets the requirements for becoming a complete Skill unit with contracts, phases, and operational guidance.
Quality Workflow Guidance: Provides a repeatable process for adding implementation code, evaluations, tests, resolver entries, and verification checks.
Use Case: When a developer creates a new AI capability, use this Skill to audit completeness, establish quality gates, and prepare the capability for reliable agent activation.

Quick Start

Use the skillify skill to review my feature and convert it into a properly structured agent skill with tests and evaluations.

Frequently Asked Questions about skillify

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

FAQPage Schema

How do I convert raw features into structured and validated agent skills?▼

To convert raw features into structured agent skills, you audit the feature for completeness, generate a SKILL.md definition, add deterministic implementations, and create supporting test artifacts for reliable agent activation.

What is the process for auditing AI capability development for skill completeness?▼

Auditing AI capability development involves evaluating whether a feature meets the requirements to become a complete Skill unit by checking for contracts, phases, operational guidance, and validation checks.

How do I set up quality workflows for testing and evaluating agent skills?▼

Setting up quality workflows for agent skills requires a repeatable process to add implementation code, evaluations, tests, and resolver entries, followed by verification checks to establish quality gates.

Do I need a SKILL.md definition to prepare my AI capability for agent activation?▼

Yes, generating a SKILL.md definition is required to prepare an AI capability for agent activation, as it provides the discovery metadata and operational workflows needed for resolver integration.

What's the best way to structure agent workflows and resolver integration for new features?▼

The best way to structure agent workflows and resolver integration is to transform raw features into structured skills by applying quality gates, generating discovery metadata, and adding validation checks.

When should I not use automation to turn a feature into an agent skill?▼

You should not use automation to turn a feature into an agent skill if the raw feature lacks clear operational guidance, making it unable to pass the audit for contracts, phases, and deterministic implementations.