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Diagram Reports Generator

Community
dmccreary
diagram-reports-generator

Generates a status report of all diagrams and MicroSims across an intelligent textbook's chapters, including difficulty, Bloom's level, and UI complexity. Use to audit visualization coverage before a content review.

Overview

Publisherdmccreary
Repositoryibook-skills
Skill namediagram-reports-generator
Stars
109
Forks
21
Bundled files
1
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by dmccreary on GitHub. Read the source before you install it.

Installation

Install the Diagram Reports Generator AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/dmccreary/ibook-skills/tree/main/skills/archived/diagram-reports-generator
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/dmccreary/ibook-skills.git /tmp/ibook-skills
mkdir -p .claude/skills
cp -r /tmp/ibook-skills/skills/archived/diagram-reports-generator .claude/skills/diagram-reports-generator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Diagram Reports Generator in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Diagram Reports Generator on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Diagram Reports Generator is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Diagram Reports Generator

Overview

This skill automatically generates comprehensive reports of all diagrams and MicroSims in the geometry course by analyzing chapter markdown files. It creates two report files: a table view for quick reference and a detailed view organized by chapter.

When to Use This Skill

Use this skill when:

  • Working with the geometry-course intelligent textbook project
  • Needing to audit all diagrams and MicroSims across chapters
  • Tracking implementation status of visual elements
  • Analyzing complexity and Bloom's Taxonomy distribution
  • Updating documentation after adding new diagrams or MicroSims
  • Generating reports for instructors or content creators

Workflow

Step 1: Install the Diagram Report Generator Script

If the project does not already have the diagram report generator script, install it from the skill's bundled resources:

  1. Check if src/diagram-reports/diagram-report.py exists in the project
  2. If it doesn't exist:
    • Create the directory: mkdir -p src/diagram-reports
    • Copy the script from this skill's scripts/diagram-report.py to src/diagram-reports/diagram-report.py
  3. If it already exists, verify it's up to date (optionally show the user a diff if there are differences)

Example installation:

bash
# Create directory if needed
mkdir -p src/diagram-reports

# Copy script from skill (use Read tool to get script content, then Write tool to create file)
# The script is located in this skill's scripts/diagram-report.py

Step 2: Verify Project Structure

Before running the report generator, verify the project structure:

  1. Confirm the current working directory is the geometry-course root (or any intelligent textbook project)
  2. Verify docs/chapters/ contains chapter directories (format: 01-chapter-name, 02-chapter-name, etc.)
  3. Ensure docs/learning-graph/ directory exists for output

Step 3: Run the Report Generator

Execute the Python script to generate the reports:

bash
python src/diagram-reports/diagram-report.py

The script will:

  • Scan all chapter directories in docs/chapters/
  • Parse each chapter's index.md file
  • Extract diagram and MicroSim specifications from <details> blocks with "#### Diagram:" headers
  • Analyze each element for:
    • Type (diagram or MicroSim)
    • Status (implementation status if specified)
    • Bloom's Taxonomy levels
    • UI element count
    • Estimated difficulty (Easy, Medium, Hard, Very Hard)
    • Learning objectives

Step 4: Verify Generated Reports

After running the script, verify two files were created in docs/learning-graph/:

  1. diagram-table.md - A sortable table view with columns:

    • Chapter number
    • Element title (with links to chapter sections)
    • Status
    • Type (Diagram/MicroSim)
    • Bloom's Taxonomy levels
    • UI elements count
    • Difficulty rating
  2. diagram-details.md - A detailed view organized by chapter with:

    • Chapter-level summaries
    • Full element descriptions
    • Learning objectives
    • Linked titles to source chapter sections

Step 5: Review Navigation Configuration

The generated reports should already be linked in the MkDocs navigation. Verify the mkdocs.yml file contains these entries under the "Learning Graph" section:

yaml
- Learning Graph:
    - Diagrams Table: learning-graph/diagram-table.md
    - Diagrams Details: learning-graph/diagram-details.md

If these entries are missing, add them to the navigation structure.

Step 6: Preview the Reports

To view the generated reports:

  1. Run mkdocs serve to start the local development server
  2. Navigate to the "Learning Graph" section
  3. Click on "Diagrams Table" or "Diagrams Details"
  4. Verify all diagrams and MicroSims are properly listed with accurate information

Understanding Report Output

Difficulty Estimation

The script estimates difficulty based on:

  • Element Type: MicroSims start with higher base difficulty
  • UI Complexity: Number of sliders, buttons, dropdowns, and other controls
  • Features: Animation, rotation, 3D/isometric views, real-time calculations
  • Canvas Size: Larger canvases increase complexity

Difficulty levels:

  • Easy: Static diagrams or simple visualizations
  • Medium: Basic interactivity with 1-3 UI elements
  • Hard: Moderate interactivity with 4-6 UI elements or complex features
  • Very Hard: High interactivity with many UI elements or advanced features

Bloom's Taxonomy Detection

The script automatically detects Bloom's Taxonomy levels mentioned in specifications:

  • Remembering
  • Understanding
  • Applying
  • Analyzing
  • Evaluating
  • Creating

Troubleshooting

No Elements Found

If the report shows zero elements:

  • Verify chapter markdown files contain #### Diagram: headers followed by <details> blocks
  • Check that <details> blocks include specification content
  • Run with verbose flag: python src/diagram-reports/diagram-report.py --verbose

Missing Information

If elements are missing type, status, or other fields:

  • Review the <details> block format in chapter files
  • Ensure specifications include **Type:**, **Status:**, and **Learning Objective:** fields
  • The script will infer type from content if not explicitly specified

Broken Links in Reports

If chapter links don't work:

  • Verify chapter directory naming follows the pattern: ##-descriptive-name
  • Check that MkDocs anchor generation matches the script's anchor creation logic
  • Test links by navigating in the served site

Advanced Usage

Custom Output Location

Specify a different output directory:

bash
python src/diagram-reports/diagram-report.py --output-dir /path/to/output

Generate CSV Format

For spreadsheet analysis:

bash
python src/diagram-reports/diagram-report.py --format csv

Generate HTML Format

For standalone HTML reports:

bash
python src/diagram-reports/diagram-report.py --format html

Verbose Output for Debugging

Enable detailed logging:

bash
python src/diagram-reports/diagram-report.py --verbose

Integration with Intelligent Textbook Workflow

This skill integrates with the broader intelligent textbook creation workflow:

  1. After content creation: Run this skill after generating or updating chapter content
  2. Before review sessions: Generate reports to identify gaps or inconsistencies
  3. During planning: Use difficulty distribution to balance implementation effort
  4. For documentation: Include reports in instructor guides or project documentation

Bundled Resources

scripts/diagram-report.py

This skill includes the complete Python script for generating diagram and MicroSim reports. The script will be installed into the user's project at src/diagram-reports/diagram-report.py when the skill is first used.

The script:

  • Analyzes all chapter markdown files in docs/chapters/
  • Extracts diagram and MicroSim specifications from <details> blocks
  • Calculates difficulty estimates based on UI complexity and features
  • Detects Bloom's Taxonomy levels from specification text
  • Generates both table and detailed report formats
  • Supports multiple output formats (markdown, CSV, HTML)

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Diagram Reports Generator AI skill do?

Generates a status report of all diagrams and MicroSims across an intelligent textbook's chapters, including difficulty, Bloom's level, and UI complexity. Use to audit visualization coverage before a content review.

Why use Diagram Reports Generator on TypingMind?

Because you install it once and use it with any model. Diagram Reports Generator is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Diagram Reports Generator in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/dmccreary/ibook-skills/tree/main/skills/archived/diagram-reports-generator. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Diagram Reports Generator?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Diagram Reports Generator?

As many as you like. As long as a model supports skills, you can use Diagram Reports Generator with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Diagram Reports Generator AI skill free?

It is published on GitHub by dmccreary. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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