Open-Sourced K-MAPS

While working on the Chessboard Magic Repertoire Builder, a fellow chess enthusiast @mattchessic mentioned that it would be great to have something called K-MAPS to help visualize and refine one’s repertoire.
At the time, I had no idea what K-MAPS were — but the concept intrigued me: a simple, visual way to assess a chess position across key strategic dimensions. Naturally, I did what any developer would do — I went searching for an existing library.
To my surprise, there wasn’t one. So, with help from the Chess Programming Wiki, some late-night AI assistance, and more than a few long coding sessions, I decided to put something together.

TLDR: GitHub Repository: https://github.com/HollowLeaf1981/chess-kmaps

What Are K-MAPS?

K-MAPS (King, Material, Activity, Pawn Structure, Space) is a conceptual framework that breaks down a chess position into five fundamental strategic factors:

• King Safety — How exposed or protected each king is.
• Material — The relative balance of pieces.
• Activity — The mobility and coordination of your forces.
• Pawn Structure — The health and formation of your pawns.
• Space — The territorial control and influence across the board.

The goal is to express a position’s strategic balance numerically — and visually — making it easier to spot imbalances, strengths, and weaknesses at a glance.

The Rabbit Hole

The first step was research — reading through the various mathematical approaches and evaluation formulas used in chess engines and theory, to understand what an implementation might look like.
However, I quickly discovered there wasn’t a definitive definition or set of algorithms for K-MAPS anywhere — at least not in a form that could be directly implemented. So, I took a best-guess approach: studying how each factor might be quantified and how those metrics could interact in a balanced way.
Next came the design. I wanted the package to be simple — just one function that takes a FEN and returns normalized scores for White and Black.
To achieve this, I relied on chess.js for basic board manipulation, while the rest would be pure logic — identifying positional attributes, computing ratios, and developing a weighting system to blend them into five unified K-MAPS metrics.
Then came the coding. I built the initial version from scratch, guided by notes, intuition, and plenty of trial and error. Once it worked, I ran the entire library through multiple AIs — ChatGPT, Grok, and Gemini — to identify possible improvements, missed conditions, or edge cases I hadn’t considered. Each iteration brought refinements until it became a stable, modular, and surprisingly robust little library.

I’m an amateur programmer at best, and with the number of algorithms and calculations involved, I’ve most definitely missed something — but that’s part of the fun. The project is open so others can explore, improve, and build on it.

The Result

The result is Chess K-MAPS — an open-source, MIT-licensed JavaScript library that evaluates a position and returns normalized scores for the five core factors.
It also includes a simple demo app so you can plug in your own positions and see how each metric shifts as the board evolves.

Image from the Chessboard Magic Repertoire Builder

It’s still a basic implementation — a foundation rather than a finished tool — but I’m hoping others in the chess and dev community will pick it up, refine it, and expand upon it. For now, it’s a simple function that takes a FEN and returns the output — clear, direct, and ready to experiment with.
GitHub Repository: https://github.com/HollowLeaf1981/chess-kmaps

Do let me know what you think — and if you end up using this in your own chess products, I’d love to hear about it.

Kind Regards,
Toan Hoang (@HollowLeaf)