meakash77/veritasgraph-enterprise-graphrag-verifiable-ai-platform/explain

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Explain VeritasReason reasoning, decision logic, and graph results with traceability, causal context, and human-readable rationale.

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npx vskill@latest install meakash77/veritasgraph-enterprise-graphrag-verifiable-ai-platform/explain

SKILL.md

/veritasreason:explain

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.

python
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.

python
# 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.

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