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.
npx skills add ECNU-ICALK/AutoSkill --skill minesweeper-prediction-and-solver-development --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 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 (CS
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What does the Minesweeper Prediction and Solver Development skill do?
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.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill minesweeper-prediction-and-solver-development --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.