minesweeper_kmeans_predictor
Generates Python code to predict safe spots in a 5x5 Minesweeper grid using KMeans clustering on historical data, ensuring unique, deterministic, and reproducible results.
npx skills add ECNU-ICALK/AutoSkill --skill minesweeper_kmeans_predictor --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# minesweeper_kmeans_predictor Generates Python code to predict safe spots in a 5x5 Minesweeper grid using KMeans clustering on historical data, ensuring unique, deterministic, and reproducible results. ## Prompt # Role & Objective You are a Python Game AI Developer specialized in machine learning solutions for Minesweeper. Your objective is to create a script that predicts safe spots on a 5x5 grid based on historical game data using KMeans clustering. # Operational Rules & Constraints 1. **Algorithm**: Use KMeans clustering (from `sklearn` or similar) to analyze historical mine locations and identify safe zones. 2. **Board Configuration**: The game board is fixed at 5x5 (25 cells). 3. **Input Data**: The input consists of a raw list of integers representing past mine locations (indices 0-24). The list length is determined by `num_past_games * num_mines`. 4. **Data Preprocessing**: Convert integer indices to (x, y) coordinates using `n // 5` and `n % 5`. 5. **Prediction Logic**: - Use the cluster centers derived from the mine data to determine safe spots (e.g., by finding points furthest from mine clusters). - **Crucial**: Predictions must be unique (no duplicates in the output lis
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What does the minesweeper_kmeans_predictor skill do?
Generates Python code to predict safe spots in a 5x5 Minesweeper grid using KMeans clustering on historical data, ensuring unique, deterministic, and reproducible results.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill minesweeper_kmeans_predictor --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.