القائمة
Query the VeritasReason knowledge graph using SPARQL, Cypher, keyword search, and structured graph query patterns.
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
VeritasReason full-stack knowledge graph skill for context graphs, decision intelligence, explainability, extraction, reasoning, visualization, ontology, provenance, policy, and export workflows.
Analyze cause-and-effect relationships in the VeritasReason knowledge graph — causal chains, interventions, counterfactuals, and causal influence scores.
Track and inspect graph changes, diffs, temporal updates, and the impact of new data on VeritasReason knowledge graphs.
Full decision lifecycle in VeritasReason � record, query, find precedents (hybrid/advanced), analyze influence, explain, insights dashboard, list, and record exceptions. Uses AgentContext, ContextGraph, DecisionQuery, CausalChainAnalyzer, DecisionRecorder.
Detect duplicate entities, duplicate groups, and relationship duplicates in VeritasReason using fuzzy matching, schema heuristics, and graph similarity.
Generate, inspect, and use node/text embeddings in VeritasReason — compute Node2Vec embeddings, find similar nodes, score link predictions, batch similarity, and pairwise similarity. Uses NodeEmbedder, SimilarityCalculator, LinkPredictor, and AgentContext. Sub-commands: compute, similar, similarity, predict-link, top-links, batch, pairwise.
| name | query |
| description | Query the VeritasReason knowledge graph using SPARQL, Cypher, keyword search, and structured graph query patterns. |
Run graph queries and search. Usage: /veritasreason:query <mode> [args]
$ARGUMENTS = query mode + query string or filter.
sparql <query>Execute a SPARQL query against the graph.
from veritasreason.query import QueryEngine
engine = QueryEngine()
results = engine.query_sparql(query)
Return: query bindings as a Markdown table.
cypher <query>Execute a Cypher-like query.
results = engine.query_cypher(query)
Output: node/relationship results and path summaries.
search <keywords> [--filter <type>]Search graph entities by keyword.
results = engine.search(keywords=keywords, filter_type=filter_type)
Return: ranked matches with entity types and relevance scores.