What problem does it solve?
This skill addresses the issue of inconsistent, unreadable, or messy SPARQL queries by providing a rigorous methodology to reformat them into a clean house style while mathematically guaranteeing that the query's logic remains identical.
Core Features & Use Cases
- Algebraic Equivalence Checking: Uses Apache Jena ARQ to prove that your cleaned query produces the exact same results as the original.
- Standardization Rules: Provides a clear set of rules for keyword casing, indentation, and prefix management to ensure team-wide consistency.
- Use Case: When refactoring complex SPARQL queries for a public API, use this skill to ensure your cleanup doesn't accidentally alter the data output or break downstream applications.
Quick Start
Use the sparqling-cleanup skill to verify that the cleaned query in after.rq is equivalent to the original query in before.rq using the public parliament endpoint.