Orchestrate SPARQL transformation chains with bundles, pivots, and augmentations.
npx skills add https://github.com/danbri/forgetmenot --skill kgx-node-flow
Please help me install this Agent Skill. Skill: kgx-node-flow Source: https://github.com/danbri/forgetmenot/tree/main/skills/kgx-node-flow Command: npx skills add https://github.com/danbri/forgetmenot --skill kgx-node-flow
This skill provides a unified implementation for managing complex data transformation chains, enabling users to wire together SPARQL endpoints, pivot between datasets, and augment information without manual query construction.
Use the kgx-node-flow skill to execute a chain spec that fetches UK MPs and pivots to their associated committee memberships.
High-intent search queries and answers about installing and using this skill.
You orchestrate complex SPARQL data transformation chains by defining sequences of Bundle transformations, including source starters, filters, pivots, and augmentations. This manages multi-endpoint data flows without manual query construction.
Building reproducible linked-data pipelines for parliamentary research requires orchestrating SPARQL-based data chains that track provenance and prevent alias collisions. This approach standardizes query generation across Parliament-operated APIs and Wikidata.
Yes, you can pivot between UK parliamentary datasets and Wikidata without manual SPARQL queries by using standardized library engines and templates. The system declaratively generates queries to augment information across endpoints.
Data flow orchestration supports provenance tracking and alias collision prevention within a unified library architecture. This ensures reproducible data pipelines when wiring together multiple SPARQL endpoints and transformations.
You need declarative query generation for SPARQL-based data flows when constructing multi-step transformation chains that require reproducible pipelines. It is essential for managing complex augmentations and pivots across linked-data endpoints.
Manual query construction for linked-data augmentation lacks built-in provenance tracking and alias collision prevention. Complex multi-endpoint data flows require a unified library architecture to maintain reproducibility and orchestrate pivots effectively.
danbriExecute SPARQL 1.1 queries against the UK Parliament DDP graph.
danbriQuery legacy UK Parliament Linked Data API endpoints for structured datasets.
danbriQuery Wikidata SPARQL for UK political figure entity resolution.
danbriCrawl the UK Parliament Thesaurus API and normalize it into N-Quads.
