Agent Skills › semantica-agi/semantica › explain

explain

GitHub

用于解释决策逻辑和图分析结果，提供可追溯的因果上下文及人类可读的推理依据。支持按ID查询决策因素、规则轨迹或节点连接关系及其上下游因果链。

plugins/skills/explain/SKILL.md semantica-agi/semantica

触发场景

用户需要理解AI或系统做出的特定决策原因用户希望查看知识图谱中节点间的关联及因果链条

安装

npx skills add semantica-agi/semantica --skill explain -g -y

更多选项

非标准路径

npx skills add https://github.com/semantica-agi/semantica/tree/main/plugins/skills/explain -g -y

不安装直接使用

npx skills use semantica-agi/semantica@explain

指定 Agent (Claude Code)

npx skills add semantica-agi/semantica --skill explain -a claude-code -g -y

安装 repo 全部 skill

npx skills add semantica-agi/semantica --all -g -y

预览 repo 内 skill

npx skills add semantica-agi/semantica --list

SKILL.md

Frontmatter

{
    "name": "explain",
    "description": "Explain Semantica reasoning, decision logic, and graph results with traceability, causal context, and human-readable rationale."
}

/semantica:explain

Produce explanations for decisions, rules, and graph analytics. Usage: /semantica:explain <target> [args]

$ARGUMENTS = explanation target + optional detail level.

`decision <decision_id> [--detail <level>]`

Explain why a decision was reached.

from semantica.reasoning.explanation_generator import ExplanationGenerator

# For decision explainability in Semantica 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.