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Dust Llm

Checked
dust-tt·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 dust-tt/dust --skill dust-llm -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

dust-tt

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 dust-tt/dust at 41e4d09697.

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

Commit
41e4d09697
Manifest
.claude/skills/dust-llm/SKILL.md
Scanned
1 file, 567 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 dust-llm skill enables AI agents to interact with LLMs through the Dust platform, facilitating advanced natural language processing and model inference tasks. It serves as a bridge allowing agents to execute complex reasoning and content generation workflows directly within the Dust ecosystem.

When to use it

  • Integrate large language models into custom agent workflows hosted on Dust for enhanced decision-making.
  • Enable agents to perform natural language understanding tasks without managing raw model infrastructure.
  • Facilitate seamless communication between autonomous agents and external LLM services via Dust's API.
  • Deploy scalable inference pipelines that leverage the stability and features of the Dust platform.

Key capabilities

  • Direct integration with LLMs through the Dust environment.
  • Support for natural language processing tasks within agent logic.
  • Simplified deployment of model inference workflows.
  • Compatibility with Dust's existing ecosystem tools and APIs.

Example prompts

  • "Use the dust-llm skill to analyze this customer feedback text and summarize key sentiment points."
  • "Generate a marketing email draft based on the product details provided using an LLM via Dust."
  • "Extract structured data from unstructured documents using the capabilities of the dust-llm integration."

Tips & gotchas

Ensure your Dust environment is properly configured with necessary API keys before invoking the skill. Performance may vary depending on the specific LLM model selected and the complexity of the input data processed by the agent.