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Fastapi Streamlit

Checked
tondevrel·vlatest·backend·MIT

This skill rapidly builds interactive web apps using FastAPI and Streamlit, streamlining development for data applications and demos.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add tondevrel/scientific-agent-skills --skill fastapi-streamlit -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

tondevrel

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned tondevrel/scientific-agent-skills at 6e89d9e8c8.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
6e89d9e8c8
Manifest
skills/fastapi-streamlit/SKILL.md
Scanned
1 file, 314 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill combines FastAPI and Streamlit to create web applications with a Python backend. It allows for the rapid development of interactive dashboards and APIs, enabling AI agents to expose functionality through user interfaces or serve data requests. The integration streamlines deployment and simplifies complex workflows involving both API endpoints and visual components.

When to use it

  • Building interactive dashboards: Visualize data analysis results or model outputs in a user-friendly format.
  • Creating simple APIs with UI: Expose backend functionality through an easy-to-use web interface.
  • Developing internal tools: Quickly prototype and deploy custom tools for team members.
  • Data exploration and visualization: Allow users to interactively explore datasets and experiment with different parameters.

Key capabilities

  • FastAPI API endpoint creation
  • Streamlit UI development
  • Combined deployment of backend and frontend components
  • Python-based implementation

Example prompts

  • "Create a Streamlit app that displays the results of this FastAPI endpoint."
  • "Build an API with a Streamlit interface to allow users to adjust parameters and see the output."
  • “Deploy a FastAPI application with a simple Streamlit dashboard for monitoring.”

Tips & gotchas

  • Requires familiarity with both FastAPI and Streamlit.
  • Deployment can be complex, requiring careful configuration of dependencies and server settings.