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Chainlit

Checked
salmanferozkhan·vlatest·ai-ml

Chainlit enables you to build conversational AI apps using Python and Langchain, streamlining chatbot development for interactive experiences.

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 salmanferozkhan/cloud-and-fast-api --skill chainlit -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

salmanferozkhan

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 salmanferozkhan/cloud-and-fast-api at 5ca8b5f473.

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

Commit
5ca8b5f473
Manifest
.claude/skills/chainlit/SKILL.md
Scanned
11 files, 3,255 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 chainlit skill provides a framework to build conversational AI applications with asynchronous callbacks and streaming capabilities. It simplifies creating user interfaces (UIs) for agents, enabling real-time interactions and feedback. This allows for more engaging and responsive agent experiences compared to traditional methods.

When to use it

  • Building interactive chatbots: Create bots that respond in real-time and display information dynamically.
  • Developing data analysis tools: Visualize data streams or results of complex computations within a conversational interface.
  • Creating AI assistants with streaming output: Enable agents to show progress updates or intermediate results as they work on tasks.
  • Rapid prototyping of agent UIs: Quickly build and iterate on user interfaces for your AI agents.

Key capabilities

  • Asynchronous callbacks
  • Streaming capabilities
  • UI framework for conversational AI applications
  • Real-time interactions

Example prompts

  • "Build a chatbot that can answer questions about my company's products."
  • "Create an interface to visualize the results of this data analysis process in real time."
  • "Show me the progress of this long calculation as it happens."

Tips & gotchas

This skill requires familiarity with Python and potentially FastAPI for backend integration. The asynchronous nature of chainlit might necessitate adjustments to existing codebases that are not designed for async operations.