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Explain VeritasReason reasoning, decision logic, and graph results with traceability, causal context, and human-readable rationale.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
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 | explain |
| description | Explain VeritasReason reasoning, decision logic, and graph results with traceability, causal context, and human-readable rationale. |
Produce explanations for decisions, rules, and graph analytics. Usage: /veritasreason:explain <target> [args]
$ARGUMENTS = explanation target + optional detail level.
decision <decision_id> [--detail <level>]Explain why a decision was reached.
from veritasreason.reasoning.explanation_generator import ExplanationGenerator
# For decision explainability in VeritasReason contexts:
decision_trace = ctx.trace_decision_explainability(decision_id=decision_id)
# For reasoning/proof explanations:
generator = ExplanationGenerator(detail_level=detail)
explanation = generator.generate_explanation(reasoning_result)
Output: decision factors, rule traces, confidence, and suggested next steps.
graph <node_id> [--path N]Explain graph relationships and why a node is connected.
# Use AgentContext explainability + causal tracing for graph-connected decisions
graph_explanation = ctx.trace_decision_explainability(decision_id=node_id)
upstream = ctx.get_causal_chain(decision_id=node_id, direction="upstream", max_depth=depth)
downstream = ctx.get_causal_chain(decision_id=node_id, direction="downstream", max_depth=depth)
Return: cause/effect chains, supporting evidence, and relevant metadata.