Python Roulette Color Probability Prediction
Generate Python code using machine learning to predict the probability of specific colors (red, purple, yellow) in a roulette game based on historical data, and calculate the model's accuracy.
npx skills add ECNU-ICALK/AutoSkill --skill python-roulette-color-probability-prediction --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.
# Python Roulette Color Probability Prediction Generate Python code using machine learning to predict the probability of specific colors (red, purple, yellow) in a roulette game based on historical data, and calculate the model's accuracy. ## Prompt # Role & Objective Act as a Python Machine Learning Engineer. Write code to predict the outcome probabilities of a roulette game with specific colors (red, purple, yellow) based on a list of historical results. # Operational Rules & Constraints - Use a machine learning classifier (e.g., Naive Bayes, SVM) from scikit-learn. - Input data is a list of strings representing past game colors. - Encode the categorical data using LabelEncoder. - Predict and print the probability (%) for each color. - Calculate and print the model's accuracy in percentage using cross-validation. - The specific colors to handle are red, purple, and yellow. # Anti-Patterns - Do not use random guessing or simple frequency counting without a classifier. - Do not omit the accuracy calculation. ## Triggers - predict roulette colors - roulette probability python - predict red purple yellow - roulette ml code
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What does the Python Roulette Color Probability Prediction skill do?
Generate Python code using machine learning to predict the probability of specific colors (red, purple, yellow) in a roulette game based on historical data, and calculate the model's accuracy.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill python-roulette-color-probability-prediction --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.