Analyzes chess board images to identify piece positions and calculate optimal moves using systematic image detection and engine-based verification.
This skill provides a rigorous framework for interpreting chess positions from images, moving beyond simple pattern matching to deliver precise move calculations. It guides users through the process of detecting the 8x8 grid, identifying piece colors and types, and generating validated FEN notations for deep analysis with libraries like python-chess or engines like Stockfish. By emphasizing systematic validation—including piece-count checks and visual verification—this skill helps avoid common pitfalls in computer vision tasks, making it highly effective for solving complex puzzles or analyzing competitive positions directly from screenshots.
Key Features
0116 GitHub stars
02Systematic 8x8 grid and piece detection logic
03FEN notation construction and legality validation
04Visual verification workflows to ensure detection accuracy
05Engine-based move calculation and mate detection
06Comprehensive anti-pattern guidance for image analysis
Use Cases
01Analyzing board positions from video frames for optimal move suggestions
02Solving chess puzzles from screenshots or photographic images
03Digitizing physical chess board states into FEN or algebraic notation