Skip to content
TrustedSkillsbeta
Back to all skills

Llm Streaming Response Handler

Checked
curiositech·vlatest·ai-ml·MIT

Provides LLMs guidance and assistance for building AI and machine learning 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 curiositech/some_claude_skills --skill llm-streaming-response-handler -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

curiositech

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 curiositech/some_claude_skills at 6713fc7a64.

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

Commit
6713fc7a64
Manifest
.claude/skills/llm-streaming-response-handler/SKILL.md
Scanned
7 files, 2,246 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 handles streaming responses from Large Language Models (LLMs). It allows for the incremental delivery of text as it's generated, providing a more responsive and engaging user experience. The handler manages the complexities of receiving and processing these streamed tokens, making them readily usable within an AI agent workflow.

When to use it

  • Long-form content generation: Ideal when generating articles, stories, or code where immediate feedback is valuable.
  • Real-time chat applications: Enhances responsiveness in conversational agents by displaying text as the LLM produces it.
  • Complex reasoning tasks: Useful for showcasing progress and providing intermediate results during intricate problem-solving processes.
  • Interactive tutorials/guides: Allows users to see instructions unfold gradually, improving comprehension and engagement.

Key capabilities

  • Handles streamed responses from LLMs.
  • Processes tokens incrementally.
  • Provides a responsive user experience.
  • Manages complexities of streaming data.

Example prompts

  • "Generate a short story about a cat exploring a garden, stream the response."
  • "Write Python code to calculate Fibonacci numbers up to 100, and show me the code as you write it."
  • "Explain the concept of quantum entanglement, streaming the explanation one sentence at a time."

Tips & gotchas

The skill requires an LLM that supports streaming responses. Ensure your chosen LLM is configured correctly for streamed output to leverage this skill effectively.