chatgpt-apps

Identify valid Skill units and extract metadata into a structured YAML block.

Updated Mar 17, 2026
One-click install
npx skills add https://github.com/jaypatrick/skills --skill chatgpt-apps-jaypatrick
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: chatgpt-apps
Source: https://github.com/jaypatrick/skills/tree/main/chatgpt-apps
Command: npx skills add https://github.com/jaypatrick/skills --skill chatgpt-apps-jaypatrick

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

ChatGPT Apps development and scaffolding can be complex and error-prone; this Skill unit metadata workflow standardizes discovery by requiring a root SKILL.md with YAML frontmatter that defines name and description, and it sets the stage for consistent metadata generation.

Core Features & Use Cases

  • Identify valid Skill units by ensuring a SKILL.md exists at the root and contains a name and description in YAML frontmatter.
  • Determine optional resources by scanning for scripts/, references/, and assets/ directories and validating their contents.
  • Produce a machine-readable manifest suitable for indexing, tooling, and downstream scaffolding workflows.

Quick Start

Provide inputs to generate the complete metadata block for each valid Skill unit following the required fields.

Frequently Asked Questions about chatgpt-apps

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

FAQPage Schema
How do I generate metadata for ChatGPT Apps SDK projects?

To scaffold ChatGPT Apps SDK projects, provide your project inputs to generate a structured YAML metadata block. This block includes name, tagline, description, complexity, category, keywords, dependencies, components, and safety assessments.

What makes a valid Skill unit for ChatGPT Apps development?

A valid Skill unit requires a root SKILL.md file containing YAML frontmatter that explicitly defines both the name and description fields. Optional components are identified by locating scripts, references, and assets directories.

How does ChatGPT Apps scaffolding identify optional components?

ChatGPT Apps scaffolding identifies optional components by scanning the project root for the presence of scripts, references, and assets directories, validating their contents to determine available resources for the unit.

Can I use this metadata workflow for ChatGPT Apps without dependencies?

Yes, you can use this workflow without dependencies. The metadata generation process extracts all fields including an empty dependencies array, returning a complete machine-readable manifest for indexing and downstream tooling.

What is the best way to standardize discovery for ChatGPT Apps SDK projects?

The best way to standardize ChatGPT Apps SDK discovery is by requiring a root SKILL.md with YAML frontmatter, ensuring consistent metadata generation across all valid units for downstream scaffolding workflows.

Why does my ChatGPT Apps metadata extraction fail to return a structured YAML block?

Metadata extraction fails when the root SKILL.md file is missing or lacks the required YAML frontmatter defining the name and description fields, preventing the generation of a structured manifest.