Agent Skills › semantica-agi/semantica › explain

explain

GitHub

用于解释Semantica系统中的决策逻辑、规则推理及图分析结果。支持通过指定ID和详细程度，生成包含因果链、置信度、证据及后续建议的可读性解释报告，提升系统透明度与可追溯性。

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

触发场景

用户询问某个决策的具体原因或依据需要分析图中节点间的连接关系及因果链条调试或审计推理过程中的规则触发情况

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.