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graphify

Build a knowledge graph from mixed files with an explicit audit trail.

10|11|Updated Feb 22, 2026
Search Tags:#document processing#knowledge graph#codebase mapping#corpus analysis#graph extraction#multimedia transcription
One-click install
npx skills add https://github.com/microsoft/amplifier-bundle-skills --skill graphify-microsoft
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: graphify
Source: https://github.com/microsoft/amplifier-bundle-skills/tree/main/skills/graphify
Command: npx skills add https://github.com/microsoft/amplifier-bundle-skills --skill graphify-microsoft

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires graphifyy, and includes references (resource) components.

What problem does it solve?

This Skill helps you transform a folder of files into a navigable knowledge graph when the source material is too large, too mixed, or too interconnected to skim efficiently by hand.

Core Features & Use Cases

  • Corpus-to-Graph Extraction: Builds a knowledge graph from code, documents, papers, images, and videos with an explicit audit trail for extracted, inferred, and ambiguous relationships.
  • Multi-Format Outputs: Produces interactive HTML, GraphRAG-ready JSON, and a plain-language report so humans and downstream tools can explore the same corpus in different ways.
  • Graph Navigation and Querying: Supports explicit graph exploration tasks such as path finding, node explanation, and query-style traversal after the graph has been built.
  • Use Case: A developer can map a large repository, a researcher can orient around a mixed document set, and a product team can quickly surface hidden connections across artifacts.

Quick Start

Use the graphify skill to build a knowledge graph for this folder and generate the report, JSON, and interactive visualization.

Frequently Asked Questions about graphify

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

FAQPage Schema
How do I build a knowledge graph from a folder of mixed files?▼

To build a knowledge graph from mixed files, use corpus-to-graph extraction to process multi-format inputs like code, documents, images, and videos, generating interactive HTML, JSON, and a plain-language report.

What is the best way to map a large codebase for orientation?▼

Mapping a large codebase is best handled by generating a navigable knowledge graph that clusters components and extracts semantic relationships, preserving an explicit audit trail of inferred and ambiguous connections.

Can I extract inferred and ambiguous relationships from a research corpus?▼

Yes, extracting inferred and ambiguous relationships from a research corpus produces a queryable graph with an explicit audit trail, allowing you to surface hidden connections across mixed document sets.

Does graphify support multimedia transcription and image extraction?▼

Graphify supports multimedia archives by applying multi-format extraction to images and videos, transforming transcribed and extracted content into a unified graph suitable for human review.

How do I navigate and query a knowledge graph after it is built?▼

Navigating a built knowledge graph involves using explicit graph exploration tasks such as path finding, node explanation, and query-style traversal to interact with the generated interactive HTML and JSON outputs.

When should I use a knowledge graph instead of ordinary Q&A for document processing?▼

You should use a knowledge graph instead of ordinary Q&A when you need one-time orientation maps for large, interconnected corpora, allowing you to explore explicit, inferred, and ambiguous relationships rather than retrieving isolated answers.

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