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Llama Cpp

Checked
zechenzhangagi·vlatest·ai-ml·MIT

Llama Cpp enables developers to utilize Meta's Llama model within C++ applications for efficient and customized AI tasks.

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 orchestra-research/ai-research-skills --skill llama-cpp -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

zechenzhangagi

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 zechenzhangagi/ai-research-skills at 773a52944b.

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

Commit
773a52944b
Manifest
12-inference-serving/llama-cpp/SKILL.md
Scanned
4 files, 615 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 provides access to and utilizes the Llama.cpp library, enabling AI agents to perform inference using quantized large language models (LLMs). It allows for running these LLMs locally with reduced resource requirements compared to traditional methods. The agent can generate text, answer questions, and engage in conversations based on the loaded model’s knowledge.

When to use it

  • Local LLM Inference: You need to run a large language model without relying on external APIs or cloud services.
  • Resource-Constrained Environments: You're working with limited memory or processing power and require efficient LLM execution.
  • Privacy Focused Tasks: The agent needs to process sensitive data locally, ensuring no information leaves the user’s environment.
  • Offline Operation: You need an AI agent that can function without an internet connection.

Key capabilities

  • Local LLM inference using quantized models
  • Reduced resource requirements for running large language models
  • Support for various quantization methods
  • Ability to load and utilize different Llama models

Example prompts

  • "Generate a short story about a cat exploring a new city."
  • "Answer the question: What is the capital of France?"
  • "Summarize this article in three sentences: [paste article text]"

Tips & gotchas

  • Ensure you have sufficient RAM to load and run the selected Llama model. The required amount will depend on the model size and quantization level.
  • The performance of the skill is directly tied to your hardware; faster processors and more memory will result in quicker response times.