Why use Turtle (TTL) instead of standard Markdown for specifications?

TTL serves as a structured source of truth that can be programmatically validated and rendered, ensuring that documentation stays in sync with the underlying architecture.

Can this skill generate Markdown from my RDF data?

Yes, it utilizes the 'cargo make speckit-render' command to transform your structured Turtle files into readable Markdown documentation automatically.

How do I validate my RDF specifications using this skill?

You can use the built-in tool via the command 'cargo make speckit-validate' to ensure your Turtle files follow the correct ontological structure.

What is RDF in the context of this Claude Code skill?

RDF (Resource Description Framework) is a standard for expressing knowledge as triples (Subject-Predicate-Object), used here to define software specifications as structured data.

Semantic RDF Modeling

Name: Semantic RDF Modeling
Author: seanchatmangpt

byseanchatmangpt

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Database Management

Models software feature specifications and domain architectures using RDF ontologies and Turtle syntax.

About

This skill empowers Claude to architect software systems using semantic web standards, specifically Resource Description Framework (RDF) and Turtle (TTL) syntax. By treating specifications as structured data, it enables precise domain modeling, feature planning, and automated documentation generation via the ggen and speckit toolchain. It is ideal for developers who require a single source of truth for their software architecture that can be programmatically validated and rendered into various formats without manual synchronization errors.

Key Features

Mastery of Turtle (TTL) syntax for semantic knowledge representation
Semantic relationship mapping for user stories and acceptance criteria
Dynamic rendering of RDF data into human-readable Markdown documentation
Automated validation of feature specifications via cargo make integration
Single source of truth workflow for architecture and domain models
1 GitHub stars

Use Cases

Creating verifiable feature specifications that sync automatically with docs
Managing software architecture plans using semantic triples for better clarity
Defining complex domain models for large-scale enterprise software

About

Key Features

Mastery of Turtle (TTL) syntax for semantic knowledge representation
Semantic relationship mapping for user stories and acceptance criteria
Dynamic rendering of RDF data into human-readable Markdown documentation
Automated validation of feature specifications via cargo make integration
Single source of truth workflow for architecture and domain models
1 GitHub stars

Use Cases

Creating verifiable feature specifications that sync automatically with docs
Managing software architecture plans using semantic triples for better clarity
Defining complex domain models for large-scale enterprise software