Can I use this for production machine learning serving?

Yes, the skill includes best practices for FastAPI model serving, Pydantic validation, and Uvicorn worker configuration to ensure your models are production-ready.

How does it handle heavy scientific computations?

The skill provides specific patterns for using FastAPI background tasks and Streamlit’s @st.cache_data to ensure UIs remain responsive even during long-running scientific processes.

What does the fastapi-streamlit skill do?

It provides specialized guidance and implementation patterns for building high-performance APIs with FastAPI and interactive frontends with Streamlit, specifically tailored for scientific and machine learning workflows.

Does it support automated data validation?

Absolutely. It emphasizes the use of Pydantic schemas in FastAPI to ensure all API inputs and outputs are strictly typed and validated, reducing runtime errors.

FastAPI & Streamlit Deployment

Name: FastAPI & Streamlit Deployment
Author: tondevrel

bytondevrel

•

Data Science & ML

Deploys scientific models and data applications using high-performance FastAPI backends and interactive Streamlit frontends.

This skill bridges the gap between scientific research and production-ready applications by combining FastAPI's asynchronous performance with Streamlit's rapid UI development capabilities. It provides Claude with specialized patterns for building RESTful APIs, validating data with Pydantic, and creating interactive dashboards for complex data visualization. Whether serving machine learning models or building internal tools for stakeholders, this skill ensures best practices for performance, type safety, and efficient data caching in a scientific context.

Key Features

01Automated documentation generation with Swagger and Redoc integration.

02Implementation patterns for background tasks and long-running scientific simulations.

03Optimized scientific data processing with efficient caching and session management.

04High-performance asynchronous API development with FastAPI and Pydantic validation.

05Rapid creation of interactive data dashboards and UIs using Streamlit widgets.

061 GitHub stars

Use Cases

01Deploying machine learning models as production-ready REST endpoints.

02Building interactive scientific dashboards for real-time data exploration and visualization.

03Developing internal tools to demonstrate complex research results to non-technical stakeholders.

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add tondevrel/scientific-agent-skills fastapi-streamlit

For use in Claude.ai and ChatGPT

Download Skill