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Developing With Streamlit

Official
streamlit·vlatest·data·Apache-2.0

Develop interactive data apps quickly using Streamlit’s intuitive tools – boosting your prototyping and deployment workflow.

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 streamlit/agent-skills --skill developing-with-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

streamlit

Matched to the vendor's own GitHub organisation.

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 failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • No undeclared network calls: failed

    Contacts 2 hosts that are neither well-known infrastructure nor declared in the skill's frontmatter: docs.astral.sh, docs.streamlit.io.

    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.

    Hosts contacted

    docs.astral.shundeclareddocs.streamlit.ioundeclared

    What it found

    • developing-with-streamlit/scripts/discover.py:185contacts docs.astral.sh
      "Install Python 3.10+ (the easiest path is `uv` — see https://docs.astral.sh/uv/),\n"
    • developing-with-streamlit/scripts/discover.py:275contacts docs.streamlit.io
      "  https://docs.streamlit.io/llms-full.txt",
    • developing-with-streamlit/scripts/discover.py:290contacts docs.streamlit.io
      "  https://docs.streamlit.io/llms-full.txt",

  • 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 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 streamlit/agent-skills at c69a265613.

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

Commit
c69a265613
Manifest
developing-with-streamlit/SKILL.md
Scanned
2 files, 321 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

The developing-with-streamlit skill enables AI agents to create and manage interactive data applications using Streamlit, a Python framework for building web apps. It supports real-time data visualization, user input handling, and deployment of dashboards with minimal code.

When to use it

  • Building a dashboard to visualize sales data in real time
  • Creating an interactive tool for users to filter and explore datasets
  • Developing a prototype for a machine learning model's output visualization

Key capabilities

  • Real-time data rendering
  • Support for widgets like sliders, buttons, and text inputs
  • Integration with Python libraries such as Pandas and Matplotlib
  • Easy deployment of apps using Streamlit Cloud or local servers

Example prompts

  • "Create a Streamlit app that displays a line chart of stock prices over time."
  • "Build an interactive dashboard where users can select a region and see corresponding sales data."
  • "Generate a Streamlit application to visualize the results of a machine learning model."

Tips & gotchas

  • Ensure Python and Streamlit are installed before using this skill.
  • Some advanced features may require additional libraries or configurations.

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