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Llm

Checked
mindrally·vlatest·ai-ml·Apache-2.0

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 mindrally/skills --skill 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

mindrally

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 mindrally/skills at 7682ca7771.

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

Commit
7682ca7771
Manifest
llm/SKILL.md
Scanned
1 file, 82 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

LLM Skill Guide

What it does

This foundational skill enables AI agents to interact with Large Language Models, allowing them to process natural language inputs and generate context-aware responses. It serves as the core interface for leveraging advanced generative capabilities within your agent's workflow.

When to use it

  • Natural Language Processing: When you need an agent to understand complex user queries or instructions written in human language.
  • Content Generation: For tasks requiring creative writing, summarization, or drafting emails and reports based on provided data.
  • Reasoning Tasks: When the agent needs to solve logic puzzles, analyze patterns, or make decisions based on textual information.
  • Conversational Interfaces: To power chatbots that require fluid, human-like dialogue rather than rigid keyword matching.

Key capabilities

  • Interprets and generates natural language text with high semantic understanding.
  • Maintains context across multi-turn conversations to provide coherent responses.
  • Adapts tone and style based on specific instructions or persona definitions.
  • Processes unstructured data to extract relevant information for downstream tasks.

Example prompts

  • "Summarize the key points from this article about renewable energy trends in 2024."
  • "Draft a professional email to a client explaining a delay in project delivery, maintaining a polite tone."
  • "Analyze the following customer feedback and identify the three most common complaints regarding our mobile app."

Tips & gotchas

Ensure your prompts are specific and include necessary context; vague instructions often lead to generic or irrelevant outputs. Be mindful of token limits when passing large amounts of text, as exceeding these can cause the model to truncate input or lose earlier context.