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Debugging Streamlit

Official
streamlit·vlatest·data·Apache-2.0

Automatically identifies and suggests fixes for common errors in Streamlit applications.

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/streamlit --skill debugging-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 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 streamlit/streamlit at 3741d77bbf.

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

Commit
3741d77bbf
Manifest
.claude/skills/debugging-streamlit/SKILL.md
Scanned
1 file, 184 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 enables AI agents to interact with Streamlit applications directly, allowing them to inspect state, modify variables, and trigger reruns within a live app session. It bridges the gap between code execution and UI interaction for debugging purposes.

When to use it

  • Debugging complex state management issues in Streamlit dashboards where variable changes aren't immediately visible.
  • Simulating user interactions to verify how an app behaves under specific input conditions without manual testing.
  • Isolating logic errors by programmatically resetting app state and re-executing specific parts of the script.
  • Validating data flow between different widgets in a multi-step Streamlit form or application.

Key capabilities

  • Direct inspection of current application state and variable values.
  • Programmatic modification of existing variables to test conditional logic.
  • Triggering app reruns to observe immediate effects of state changes.
  • Integration with the Streamlit runtime environment for real-time feedback.

Example prompts

  • "Inspect the current value of the user_data variable in this running Streamlit app."
  • "Set the is_logged_in flag to false and rerun the app to see how the dashboard updates."
  • "Trigger a rerun of the script after modifying the filter_criteria input to check for errors."

Tips & gotchas

Ensure the target Streamlit application is running in an environment accessible to the AI agent's execution context. This skill is specifically designed for debugging; do not use it to alter production data or persist changes outside the session scope.