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math-graph-knowledge

Solve graph-theory and knowledge-graph reasoning tasks for provenance chains and trust networks.

2|Updated May 26, 2026
Search Tags:#knowledge-graph#graph-theory#network-analysis#ontology-engineering#provenance-mathematics#formal-knowledge-representation
One-click install
npx skills add https://github.com/r-irbe/proof-skills --skill math-graph-knowledge
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: math-graph-knowledge
Source: https://github.com/r-irbe/proof-skills/tree/main/skills/math-graph-knowledge
Command: npx skills add https://github.com/r-irbe/proof-skills --skill math-graph-knowledge

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Graph-theory and knowledge-graph reasoning for provenance chains, trust networks, knowledge lifecycles, and formal knowledge representation.

Core Features & Use Cases

  • Provides foundations for graph-theoretic analysis, knowledge-graph modeling, and provenance mathematics.
  • Supports workflows for network metrics, ontology encoding, and causal graph considerations.
  • Offers structured handoffs to specialized domains (Lean math discrete, knowledge formalization, zettelkasten) for integrated research.

Quick Start

Load the full handbook from references/math-graph-knowledge-handbook.md when convened to access the detailed sections.

Frequently Asked Questions about math-graph-knowledge

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I model a knowledge graph for provenance chains and trust networks?▼

Knowledge graph modeling for provenance chains and trust networks is handled by providing formal foundations for graph-theoretic analysis, DAGs, and provenance mathematics. It supports network metrics and causal graph workflows.

What is the best way to formalize ontology engineering within a knowledge lifecycle?▼

Ontology engineering within a knowledge lifecycle is supported through structured handoffs to formalization workflows. It enables ontology encoding and integrates with specialized domains for research formalization.

Can I use this approach for network analysis on directed acyclic graphs (DAGs)?▼

Network analysis on DAGs is fully supported as a core feature. The Skill applies graph-theory reasoning to directed acyclic graphs, calculating network metrics and evaluating causal graph considerations.

Does knowledge graph reasoning work with discrete math and zettelkasten systems?▼

Knowledge graph reasoning integrates with discrete math and zettelkasten systems through structured handoffs. It provides reference integration for formal knowledge representation across these specialized domains.

When do I need formal knowledge representation for graph-theoretic analysis?▼

Formal knowledge representation for graph-theoretic analysis is needed when solving tasks involving provenance chains, trust networks, and knowledge lifecycles. It ensures clear handoffs and structured reference integration.

Are there limitations when applying causal graph considerations to ontology modeling?▼

Causal graph considerations applied to ontology modeling require clear handoffs to specialized domains for advanced scenarios. The Skill provides foundations but directs complex formalization to integrated research workflows.

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