learning-graph-generator

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---
name: learning-graph-generator
description: Generates a comprehensive learning graph from a course description, including 200 concepts with dependencies, taxonomy categorization, and quality validation reports. Use this when the user wants to create a structured knowledge graph for educational content.
---

# Learning Graph Generator

**Version:** 0.05

You are tasked with generating a comprehensive high-quality learning graph from a course description.
A learning graph is the foundational data structure for intelligent textbooks that can recommend learning paths.
A learning graph is like a roadmap of Concepts to help students achieve their learning goals.
A learning graph is an DAG Concept graph.  Each arrow is a "Learning Dependency" relationship that suggest learning order.
The markdown you generate must be compatible with the mkdocs version of markdown.  Make sure you put a blank line before any lists.

Follow these steps carefully:

## Markdown Generation Rules

1. Always place a blank line before any markdown list.  This is required by the mkdocs markdown tools.

## Mkdocs Navigation Rules

After you add a markdown file (any file with an extension `.md`) make sure to add that file
to the navigation structure in the mkdocs.yml file.  Here is an example of the nav section
for the learning graph section:

```yml
  - Learning Graph:
    - Introduction: learning-graph/index.md
    - Course Description Assessment: learning-graph/course-description-assessment.md
    - Concept Enumeration: learning-graph/list-concepts.md
    - Graph Quality Analysis: learning-graph/graph-quality-analysis.md
    - Concept Taxonomy: learning-graph/concept-taxonomy.md
    - Taxonomy Distribution Report: learning-graph/taxonomy-distribution-report.md
```

## Step 0: Setup

Tell the user that they are running the version graph generator and the version number above.

The default context is that the skill is run from claude code in the home directory of an intelligent textbook that has been checked out from GitHub.
There should be a docs directory with a standard mkdocs.yml file in the home git directory.
You will create a directory called /docs/learning-graph it it does not already exist.  
The path is relative to the git home directory.  The assumption is that /docs is relative to the directory that claude was started in.

`mkdir -p docs/learning-graph; cd docs/learning-graph`

You will copy python programs from this skill package into the `/docs/learning-graph` directory.  
You will execute python from that directory.

If you do not see the `docs` directory and the `mkdocs.yml` file suggest that the user clone a sample textbook from the following location:

`git clone https://github.com/dmccreary/intelligent-book-template`
`cd intelligent-book-template`

## Step 1: Course Description Quality Assessment

Before you begin this step, verify that it has not already been done.
To do this, check the yml metadata in the `docs/course-description.md` file.

Here is a sample of the yml metadata:

```markdown
---
title: Course Description
description: A detailed course description 
quality_score: 95
---
# Course Description
```

If you see a quality_score above 85 you may tell the user you found a score above 85 and skip this entire step.  Tell them this is a way to save tokens.

If the quality score is below 85, analyze the provided course description at [course-description.md](../course-description.md) to ensure it has enough content to generate at a minimum of 200 high-quality concepts:

1. Verify the course has a title, prerequisites, intended audience, objectives, and outcomes ("After this course students will be able to").  If these fields are missing ask the user for this information. 
1. Examine the depth and breadth of topics covered
2. Assess whether the material has sufficient granularity for at a minimum of 200 distinct concepts
3. Check for diverse topic areas and learning objectives
4. Provide detailed feedback to the user about:
   - List t