lousybook-01/lousybooks-data-science-and-machine-learning-assistant

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public

Published on 3/7/2025

General AI

Idk, made by Lousybook01

Rules

Prompts

Models

Context

Models

Missing models (4)

This assistant is using the following blocks which have been deleted:

sambanova/llama-3.1-tulu-3-405b
sambanova/meta-llama-3.2-90b-vision-instruct
sambanova/qwen2.5-coder-32b-instruct
sambanova/qwen2.5-72b-instruct

Codestral

mistral

Voyage AI rerank-2

voyage

voyage-code-3

voyage

Gemini 2.0 Flash

gemini

1048kinput·8.192koutput

DeepSeek-R1-Distill-Llama 70B

sambanova

DeepSeek R1

sambanova

Rules

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

- You are an Angular developer
- Use Angular CLI for project scaffolding
- Use TypeScript with strict mode enabled
- Use RxJS for state management and async operations
- Use the typical naming conventions:
  - Components: .component.ts
  - Services: .service.ts
  - Pipes: .pipe.ts
  - Module: .module.ts
  - Test: .spec.ts
  - Directives: .directive.ts

- Follow Django style guide
- Avoid using raw queries
- Prefer the Django REST Framework for API development
- Prefer Celery for background tasks
- Prefer Redis for caching and task queues
- Prefer PostgreSQL for production databases

- You are a Svelte developer
- Use SvelteKit for the framework
- Use TailwindCSS for styling
- Use TypeScript
- Use the canonical SvelteKit file structure:
  ```
  src/
    actions/
    components/
    data/
    routes/
    runes/
    styles/
    utils/

Docs

Condahttps://docs.conda.io/en/latest/

Jupyterhttps://docs.jupyter.org/en/latest/

Matplotlibhttps://matplotlib.org/stable/

NumPyhttps://numpy.org/doc/stable/

Pandashttps://pandas.pydata.org/docs/

Pythonhttps://docs.python.org/3/

PyTorchhttps://pytorch.org/docs/stable/index.html

Sveltehttps://svelte.dev/docs/svelte

Reacthttps://react.dev/reference/

SvelteKithttps://svelte.dev/docs/kit

Nuxt.jshttps://nuxt.com/docs

Zodhttps://zod.dev/

Solidityhttps://docs.soliditylang.org/en/v0.8.0/

Vue docshttps://vuejs.org/v2/guide/

Next.jshttps://nextjs.org/docs/app

Angular Docshttps://angular.io/docs

Ethereumhttps://ethereum.org/en/developers/docs/

Prompts

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:

Data Pipeline Development

Create robust and scalable data processing pipelines

Generate a data processing pipeline with these requirements:

Input:
- Data loading from multiple sources (CSV, SQL, APIs)
- Input validation and schema checks
- Error logging for data quality issues

Processing:
- Standardized cleaning (missing values, outliers, types)
- Memory-efficient operations for large datasets
- Numerical transformations using NumPy
- Feature engineering and aggregations

Quality & Monitoring:
- Data quality checks at key stages
- Validation visualizations with Matplotlib
- Performance monitoring

Structure:
- Modular, documented code with error handling
- Configuration management
- Reproducible in Jupyter notebooks
- Example usage and tests

The user has provided the following information:

Context

@code

Reference specific functions or classes from throughout your project

@docs

Reference the contents from any documentation site

@diff

Reference all of the changes you've made to your current branch

@terminal

Reference the last command you ran in your IDE's terminal and its output

@problems

Get Problems from the current file

@folder

Uses the same retrieval mechanism as @Codebase, but only on a single folder

@codebase

Reference the most relevant snippets from your codebase

@url

Reference the markdown converted contents of a given URL

@currentFile

Reference the currently open file

@open

Reference the contents of all of your open files

@clipboard

Reference recent clipboard items

@os

Reference the architecture and platform of your current operating system

@commit

@repo-map

Reference the outline of your codebase

@file

Reference any file in your current workspace

Data

No Data configured

MCP Servers

Exa

npx -y exa-mcp-server

Memory

npx -y @modelcontextprotocol/server-memory

Playwright

npx -y @executeautomation/playwright-mcp-server

Filesystem

npx -y @modelcontextprotocol/server-filesystem ${{ secrets.lousybook-01/lousybooks-data-science-and-machine-learning-assistant/anthropic/filesystem-mcp/PATH }}