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Explain VeritasReason reasoning, decision logic, and graph results with traceability, causal context, and human-readable rationale.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
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npx skills add https://github.com/bibinprathap/VeritasGraph --skill explain命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
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基于 SOC 职业分类
| 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.