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Llama Cpp

Checked
davila7·vlatest·ai-ml·MIT

LlamaCpp enables running Meta’s LLaMA language model locally using C++, offering privacy and offline capabilities for developers.

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 davila7/claude-code-templates --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

davila7

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 davila7/claude-code-templates at 79182c51fb.

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

Commit
79182c51fb
Manifest
cli-tool/components/skills/ai-research/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

The llama-cpp skill enables AI agents to interact with and utilize the llama.cpp library, a high-performance C++ implementation of Llama models. It facilitates local model inference and execution within automated workflows.

When to use it

  • Deploying lightweight LLMs directly on edge devices or servers with limited resources.
  • Running private inference tasks where data must remain strictly local without cloud exposure.
  • Integrating open-source Llama architectures into custom Python-based automation scripts.
  • Optimizing model performance by leveraging quantization techniques for faster processing speeds.

Key capabilities

  • Access to the llama.cpp library for efficient C++-backed model execution.
  • Support for running various Llama model variants locally.
  • Integration capabilities within davila7/claude-code-templates workflows.
  • Utilization of quantized models to reduce memory footprint and latency.

Example prompts

  • "Configure the llama-cpp environment to load a specific quantized Llama model for local inference."
  • "Write a script using llama-cpp to process text inputs and generate responses without external API calls."
  • "Optimize the llama-cpp setup to run efficiently on a CPU-only machine with 8GB RAM."

Tips & gotchas

Ensure your system meets the minimum hardware requirements for running C++ based inference engines, particularly regarding RAM availability. Be aware that performance varies significantly depending on the specific model quantization level chosen; higher quantization reduces memory usage but may slightly impact accuracy.