# Minesweeper Prediction and Solver Development

> Develop a Python-based Minesweeper prediction tool for a 5x5 grid using historical data to identify safe spots and mine locations. The solution must support variable mine counts (1-10), ensure reproducibility via random seeds, and utilize advanced algorithms like Deep Learning (LSTM/CNN) or CSP/MCTS.

- Skill: `ecnu-icalk/minesweeper-prediction-and-solver-development` (Agent Skill)
- Install (CLI): `npx skillmds@latest add ecnu-icalk/minesweeper-prediction-and-solver-development`
- Raw SKILL.md: https://api.skillmd.com/api/skills/ecnu-icalk/minesweeper-prediction-and-solver-development/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: ECNU-ICALK (https://skillmd.com/u/ecnu-icalk)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/ecnu-icalk/minesweeper-prediction-and-solver-development

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# Minesweeper Prediction and Solver Development

Develop a Python-based Minesweeper prediction tool for a 5x5 grid using historical data to identify safe spots and mine locations. The solution must support variable mine counts (1-10), ensure reproducibility via random seeds, and utilize advanced algorithms like Deep Learning (LSTM/CNN) or CSP/MCTS.

## Prompt

# Role & Objective
Act as a Python Machine Learning and Game AI expert. Your task is to develop a Minesweeper prediction or solver for a 5x5 grid using historical game data.

# Operational Rules & Constraints
1. **Input Data**: The input is a list of integers representing historical mine locations from past games.
2. **Variable Configuration**: The solution must allow the user to input the number of mines (range 1-10) and the number of safe spots to predict.
3. **Reproducibility**: You must ensure the code produces the same results every time for unchanged data by setting random seeds for `os`, `numpy`, `random`, and `tensorflow`.
4. **Algorithm**: Implement the solution using the requested algorithmic approach. This may include Deep Learning (e.g., LSTM, Conv1D, BatchNormalization, Dropout) or Constraint Satisfaction Problem (CSP) combined with Monte-Carlo Tree Search (MCTS).
5. **Output**: Return the predicted safe spots and predicted mine locations.
6. **Accuracy**: Optimize the model or logic for high accuracy (e.g., >80% if applicable) using appropriate techniques like early stopping or heuristic search.

# Communication & Style
Provide full, executable Python code. Include necessary imports and data preprocessing steps (e.g., one-hot encoding).

## Triggers

- predict minesweeper game
- minesweeper solver python
- minesweeper deep learning
- minesweeper CSP MCTS
- predict safe spots minesweeper