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knowledge-graph-research

Analyze document collections into knowledge graphs with concept clustering and discourse-gap detection.

6|Updated Feb 5, 2026
Search Tags:#knowledge-graph#due-diligence#graph-analysis#credit-documents#discourse-analysis#graph-rag#investment-theses
One-click install
npx skills add https://github.com/maschad/my-claude --skill knowledge-graph-research
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: knowledge-graph-research
Source: https://github.com/maschad/my-claude/tree/main/skills/knowledge-graph-research
Command: npx skills add https://github.com/maschad/my-claude --skill knowledge-graph-research

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Convert unstructured credit-market documents into structured knowledge graphs to surface conceptual relationships, thematic clusters, and discourse gaps for faster due diligence and smarter investment decisions.

Core Features & Use Cases

  • Knowledge-graph construction: Transform document collections into graphs where concepts are nodes and co-occurrences define edges.
  • Discourse gap detection: Identify missing connections and bridge concepts to reveal blind spots.
  • Concept clustering: Group related ideas into coherent themes and track their evolution across time.
  • Graph-RAG insights: Use graph topology to ground AI responses and synthesize cross-document intelligence.
  • Visualization & export: Produce DOT graphs and entity/concept views for presentations and reporting.
  • Use cases: Credit covenants analysis, market research, deal diligence, and investment-thesis development.

Quick Start

Analyze the attached credit documents and generate a knowledge graph with key themes, clusters, and gaps.

Frequently Asked Questions about knowledge-graph-research

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

FAQPage Schema
How do I build a knowledge graph from credit documents to find thematic gaps?▼

Build a knowledge graph from credit documents by transforming unstructured text collections into conceptual nodes and co-occurrence edges. This process surfaces thematic clusters and identifies discourse gaps to reveal blind spots for due diligence.

Can I use discourse analysis to identify missing connections in investment research files?▼

Discourse analysis identifies missing connections in investment research by detecting conceptual gaps within document collections. It reveals blind spots by finding bridge concepts that are absent, helping refine investment theses.

How does Graph-RAG synthesize cross-document intelligence for due diligence?▼

Graph-RAG synthesizes cross-document intelligence for due diligence by using graph topology to ground AI responses. This approach leverages the structural relationships within knowledge graphs to generate insights across multiple credit-market files.

What is the best way to visualize conceptual relationships extracted from market research?▼

Visualize conceptual relationships from market research by generating DOT-exportable graphs and entity views. These visualizations represent concept clusters and connections, making thematic structures accessible for presentations and reporting.

Do I need unstructured text collections to perform concept clustering for investment theses?▼

Unstructured text collections are required to perform concept clustering for investment theses. The Skill processes these documents to group related ideas into coherent themes and track their evolution across time.

Can I export knowledge graph visualizations for credit covenant analysis reporting?▼

Export knowledge graph visualizations for credit covenant analysis by producing DOT graphs and entity concept views. These exports support reporting needs by structuring the thematic relationships found within the analyzed documents.

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