Agent Skills › semantica-agi/semantica › explain

explain

GitHub

用于解释决策逻辑、规则推理及图分析结果，提供可追溯的因果上下文和人类可读的理由。支持针对特定决策ID或图节点ID生成包含因素、置信度、因果链及建议步骤的详细解释。

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

Trigger Scenarios

用户询问某个决策背后的原因需要解释图中节点之间的连接关系请求查看推理过程的详细证据

Install

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

More Options

Non-standard path

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

Use without installing

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.