Agent Skills › semantica-agi/semantica › causal

causal

GitHub

用于在Semantica知识图谱中分析因果关系，包括构建因果链、评估干预影响、追溯反事实根因及计算因果影响力得分。

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

Trigger Scenarios

需要分析决策的因果链条或上下游影响模拟特定行动对系统的影响并评估干预效果追溯事件的根本原因或历史因果路径

Install

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

More Options

Non-standard path

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

Use without installing

npx skills use semantica-agi/semantica@causal

指定 Agent (Claude Code)

npx skills add semantica-agi/semantica --skill causal -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": "causal",
    "description": "Analyze cause-and-effect relationships in the Semantica knowledge graph — causal chains, interventions, counterfactuals, and causal influence scores."
}

/semantica:causal

Analyze causal relationships and infer impacts. Usage: /semantica:causal <task> [args]

$ARGUMENTS = task + optional target entity, filter, or intervention.

`chain [--subject <node>] [--depth N]`

Build and inspect causal chains for a subject or category.

from semantica.context.causal_analyzer import CausalChainAnalyzer
from semantica.context import AgentContext

# Option 1: Use an existing AgentContext decision backend
chain = ctx.get_causal_chain(
    decision_id=decision_id,
    direction="upstream",
    max_depth=depth,
)

# Option 2: Use CausalChainAnalyzer directly
analyzer = CausalChainAnalyzer(graph_store=ctx.knowledge_graph)
downstream = analyzer.get_causal_chain(
    decision_id=decision_id,
    direction="downstream",
    max_depth=depth,
)

Output: chain steps, cause strength, effect reach, and summary graph.

`intervene <node> <action> [--scenario <json>]`

Analyze decision impact and influenced decisions (current causal API).

analyzer = CausalChainAnalyzer(graph_store=ctx.knowledge_graph)
impact_score = analyzer.get_causal_impact_score(decision_id=decision_id)
influenced = analyzer.get_influenced_decisions(
    decision_id=decision_id,
    max_depth=depth,
)

Return: impact score, influenced decisions, and downstream scope.

`counterfactual <fact> [--weight N]`

Trace root causes and temporal causal paths.

analyzer = CausalChainAnalyzer(graph_store=ctx.knowledge_graph)
roots = analyzer.find_root_causes(decision_id=decision_id, max_depth=depth)
historical_chain = analyzer.trace_at_time(
    event_id=decision_id,
    at_time="2026-01-01T00:00:00Z",
    direction="upstream",
    max_depth=depth,
)

Output: root decision lineage and time-bounded causal context.