القائمة
Store and query Croissant datasets in a Neo4j Graph Database for relational discovery and semantic search.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Fetch and store transcripts from YouTube videos for deep content analysis.
Specialized in the MLCommons Croissant metadata specification. Can generate, validate, and serialize dataset metadata into compliant JSON-LD.
Universal Numeric Fingerprint (UNF) generator. For strings, it splits into words and sorts them alphabetically to provide order-invariant fingerprints. Supports dataframes and files too.
Send results and data files to stakeholders via email.
The Visual Systems Architect is an expert in translating complex technical requirements and infrastructure setups into structured, visually intuitive architectural diagrams (Mermaid.js).
Secure GitHub Orchestrator for Croissant Toolkit. Connect any repository, discovery skills, and audit ODRL sovereignty status.
| name | neo4j_expert |
| description | Store and query Croissant datasets in a Neo4j Graph Database for relational discovery and semantic search. |
The Neo4j Expert skill transforms hierarchical Croissant JSON-LD files into a knowledge graph. This allows users to discover relationships between datasets, creators, locations, and keywords using powerful graph queries.
Set your Neo4j credentials in the environment:
export NEO4J_URI="bolt://your-neo4j-instance:7687"
export NEO4J_USER="neo4j"
export NEO4J_PASSWORD="your-strong-password"
python3 skills/neo4j_expert/scripts/ingest.py "./data/croissant/my_dataset.jsonld"
python3 skills/neo4j_expert/scripts/query.py "Which datasets mention Kyiv?"
Dataset, Person, Organization, Place, Keyword, FileObject.(:Person/Organization)-[:CREATOR_OF]->(:Dataset)(:Dataset)-[:SPATIAL_COVERAGE]->(:Place)(:Dataset)-[:HAS_KEYWORD]->(:Keyword)(:Dataset)-[:HAS_DISTRIBUTION]->(:FileObject)