concept-cartographer

Auto-generate prerequisite maps for learning any topic — shows what to learn first, what depends on what, and the optimal learning path. Use when starting to learn something new and feeling lost about where to begin.

Overview

Concept Cartographer builds prerequisite maps for any topic. When you want to learn something new — say, "quantum computing" or "macroeconomics" — it maps out what you need to know first, what builds on what, and creates an optimal learning sequence. It eliminates the "where do I start?" paralysis that stops most self-learners.

The tool constructs a directed acyclic graph (DAG) of concepts, identifies your current knowledge baseline, and generates a personalized learning path that respects prerequisite dependencies.

When to Use

• You want to learn a complex topic but don't know the prerequisites
• You're overwhelmed by the breadth of a new field and need a sequence
• You want to identify gaps in your knowledge before tackling an advanced topic
• You're planning a self-study curriculum for a new skill or domain
• Don't use for: single-task tutorials ("how to set up nginx") — use when the topic has a learning curve with multiple prerequisites

How It Works

1. Concept Graph — Define topics and their prerequisites as a DAG
2. Topological Sort — Compute valid learning orders that respect dependencies
3. Knowledge Audit — Mark which concepts you already know
4. Path Finding — Generate the shortest path from your current knowledge to the target
5. Critical Path — Identify the longest prerequisite chain (the bottleneck)
6. Export — Render the map as JSON, text tree, or Mermaid diagram for visualization

Quick Start

bash

# Map prerequisites for a topic using the built-in knowledge base
python scripts/cartographer.py map "machine learning"

# Generate a learning path from your current knowledge
python scripts/cartographer.py path "machine learning" --known "python,basic-math,statistics"

# Visualize the concept map as a Mermaid diagram
python scripts/cartographer.py visualize "machine learning" --format mermaid

# Audit your knowledge — find gaps before starting
python scripts/cartographer.py audit "quantum computing" --known "linear-algebra,python"

# List all topics in the knowledge base
python scripts/cartographer.py topics

Built-in Knowledge Base

The tool ships with prerequisite maps for common domains:

• Programming: Python, JavaScript, SQL, Algorithms, Data Structures
• Math: Calculus, Linear Algebra, Statistics, Discrete Math
• ML/AI: Machine Learning, Deep Learning, NLP, Computer Vision
• Science: Physics, Chemistry, Biology, Quantum Computing
• Business: Economics, Accounting, Finance, Marketing
• Web: HTML, CSS, React, Node.js, Databases

You can also define custom concept graphs (see references/custom-graphs.md).

Workflow: Planning a Learning Journey

Step 1: Map the territory

bash

python scripts/cartographer.py map "deep learning"

This shows the full prerequisite tree — everything you'd eventually need to know.

Step 2: Audit your current knowledge

bash

python scripts/cartographer.py audit "deep learning" --known "python,linear-algebra,basic-statistics"

This highlights what you already know (✓), what you're missing (✗), and what's partially covered (~).

Step 3: Generate your personalized path

bash

python scripts/cartographer.py path "deep learning" --known "python,linear-algebra" --output my_plan.json

This produces the optimal sequence, skipping what you know and focusing on gaps.

Step 4: Visualize

bash

python scripts/cartographer.py visualize "deep learning" --format mermaid --output diagram.md

Paste the Mermaid output into any Markdown viewer to see the concept map.

Learning Path Output

text

🎯 Target: Deep Learning
📊 Current knowledge: 2 concepts (python, linear-algebra)
⏱️  Estimated new concepts to learn: 8
🛤️  Critical path length: 6 steps

LEARNING PATH:
  1. ☐ Calculus (prerequisites: ✓) — derivatives needed for gradient descent
  2. ☐ Probability (prerequisites: ✓) — foundational for ML
  3. ☐ Statistics (prerequisites: ✓ probability)
  4. ☐ Machine Learning Basics (prerequisites: ✓ python, ☐ statistics)
  5. ☐ Neural Networks (prerequisites: ☐ ML basics, ☐ linear algebra ✓)
  6. ☐ Deep Learning (prerequisites: ☐ neural networks)
     └── 🎯 TARGET REACHED

Common Pitfalls

1. Skipping prerequisites to save time. The critical path exists for a reason. Skipping calculus to learn ML leads to confusion and slower progress overall.
2. Trying to learn everything at once. The path is sequential for a reason. Master each step before moving to the next.
3. Overestimating your knowledge. Be honest in the audit. "I watched a YouTube video about it" ≠ "I know it."
4. Ignoring the critical path. The longest dependency chain determines your minimum time to competence. Focus there first.
5. Treating the map as gospel. Prerequisites are guidelines, not laws. Some people learn best non-linearly — adjust as you go.

Verification Checklist

• cartographer.py map "machine learning" prints the prerequisite tree
• cartographer.py path "machine learning" --known "python" generates a learning sequence
• cartographer.py audit "quantum computing" --known "linear-algebra" shows gaps
• cartographer.py visualize "machine learning" --format mermaid produces Mermaid syntax
• cartographer.py topics lists all topics in the knowledge base

References

• references/custom-graphs.md — how to define your own concept maps
• references/learning-theory.md — the cognitive science of prerequisite sequencing