schema

Generate structured knowledge schemas from text, JSON, Markdown, and code.

7|3|Updated Jan 15, 2026
One-click install
npx skills add https://github.com/Zpankz/mcp-skillset --skill schema-zpankz
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: schema
Source: https://github.com/Zpankz/mcp-skillset/tree/main/schema
Command: npx skills add https://github.com/Zpankz/mcp-skillset --skill schema-zpankz

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires Jinja2, PyYAML, spaCy, en_core_web_sm, networkx, pandas, tree-sitter, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Converts unstructured inputs (text, JSON, Markdown, code) into structured knowledge schemas and ontologies, saving time and reducing manual drafting effort.

Core Features & Use Cases

  • Extracts hierarchical structures and semantic relationships from diverse inputs and outputs Obsidian-compatible Markdown with rich frontmatter and mermaid diagrams.
  • Exports to multiple semantic formats (JSON-LD, RDF/OWL, Cypher, GraphQL) for integration with databases and knowledge graphs.
  • Supports fractal and free-mode generation with property inheritance and multi-dimensional navigation for scalable knowledge architectures.
  • Use case: a team collects notes, API specifications, and data dictionaries and needs a browsable ontology to discover connections and downstream dependencies.

Quick Start

Provide input content and let the skill generate a complete knowledge schema in Obsidian markdown and export formats.

Frequently Asked Questions about schema

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

FAQPage Schema
How do I convert unstructured text and code into a structured knowledge graph ontology?

To build a structured knowledge graph ontology from unstructured text, JSON, Markdown, or code, this Skill applies a four-layer pipeline of structure extraction, semantic analysis, and template-based output. It automatically reveals hierarchical structures and semantic relationships for downstream integration.

Can I export an Obsidian Markdown knowledge schema to JSON-LD, RDF/OWL, and Cypher formats?

Yes, you can export Obsidian Markdown knowledge schemas to JSON-LD, RDF/OWL, Cypher, and GraphQL formats. The pipeline outputs rich frontmatter and mermaid diagrams alongside these semantic formats for direct integration with databases and knowledge graphs.

Does this ontology generation approach support property inheritance and multi-dimensional navigation?

Ontology generation supports both fractal and free modes, enabling property inheritance and multi-dimensional navigation for scalable knowledge architectures. This allows teams to discover connections and downstream dependencies across complex documents and data dictionaries.

What is the best way to generate semantic metadata from API specifications and data dictionaries?

Generating semantic metadata from API specifications and data dictionaries is best handled by applying semantic analysis with optional LLM enrichment to extract relationships. This process creates a browsable ontology that reveals connections across diverse technical inputs.

Do I need spaCy and networkx to extract hierarchical structures from Markdown documents?

Yes, spaCy, networkx, and tree-sitter are required dependencies for extracting hierarchical structures and semantic relationships from Markdown documents. These libraries drive the structure extraction and semantic analysis layers of the processing pipeline.