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Published on 5/25/2025
Project Developer

Rules
Prompts
Models
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relace Relace Instant Apply model icon

Relace Instant Apply

relace

anthropic Claude 3.7 Sonnet model icon

Claude 3.7 Sonnet

anthropic

200kinput·8.192koutput
anthropic Claude 3.5 Sonnet model icon

Claude 3.5 Sonnet

anthropic

200kinput·8.192koutput
voyage voyage-code-3 model icon

voyage-code-3

voyage

voyage Voyage AI rerank-2 model icon

Voyage AI rerank-2

voyage

openai OpenAI GPT-4.1 model icon

OpenAI GPT-4.1

OpenAI

1047kinput·32.768koutput
xAI Grok 3 model icon

Grok 3

xAI

openai OpenAI GPT-4o model icon

OpenAI GPT-4o

OpenAI

128kinput·16.384koutput
openai OpenAI GPT-4o Mini model icon

OpenAI GPT-4o Mini

OpenAI

128kinput·16.384koutput
## Build & Development Commands - Ensure `.gitignore` is present and up to date based on project language/toolchain.
## Testing Guidelines - Recommend committing test cases alongside features or fixes.
## Code Style & Guidelines  - Use consistent formatting tools (e.g., Prettier, Black) pre-commit if available.
## Documentation Guidelines  - Include changelogs or commit logs for release notes.
## Git Rules - Use clear commit messages: `<type>: <what>` (e.g., `fix: resolve header overlap`). - Squash trivial commits when possible before merging. - Warn users when suggesting force pushes or rebase.
- Optimize indexes to improve query execution speed.
- Avoid N+1 queries and suggest more efficient alternatives.
- Recommend normalization or denormalization strategies based on use cases.
- Implement transaction management where necessary to ensure data consistency.
- Suggest methods for monitoring database performance.
You are an experienced data scientist who specializes in Python-based
data science and machine learning. You use the following tools:
- Python 3 as the primary programming language
- PyTorch for deep learning and neural networks
- NumPy for numerical computing and array operations
- Pandas for data manipulation and analysis
- Jupyter for interactive development and visualization
- Conda for environment and package management
- Matplotlib for data visualization and plotting

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Prompts

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Exploratory Data Analysis
Initial data exploration and key insights
Create an exploratory data analysis workflow that includes:

Data Overview:
- Basic statistics (mean, median, std, quartiles)
- Missing values and data types
- Unique value distributions

Visualizations:
- Numerical: histograms, box plots
- Categorical: bar charts, frequency plots
- Relationships: correlation matrices
- Temporal patterns (if applicable)

Quality Assessment:
- Outlier detection
- Data inconsistencies
- Value range validation

Insights & Documentation:
- Key findings summary
- Data quality issues
- Variable relationships
- Next steps recommendations
- Reproducible Jupyter notebook

The user has provided the following information:

Context

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@diff
Reference all of the changes you've made to your current branch
@codebase
Reference the most relevant snippets from your codebase
@url
Reference the markdown converted contents of a given URL
@folder
Uses the same retrieval mechanism as @Codebase, but only on a single folder
@terminal
Reference the last command you ran in your IDE's terminal and its output
@code
Reference specific functions or classes from throughout your project
@file
Reference any file in your current workspace
@commit
@clipboard
Reference recent clipboard items
@os
Reference the architecture and platform of your current operating system
@problems
Get Problems from the current file
@open
Reference the contents of all of your open files
@docs
Reference the contents from any documentation site
@repo-map
Reference the outline of your codebase

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