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Llama Cpp

Checked
ovachiever·vlatest·ai-ml

Llama Cpp converts Llama model prompts into optimized C++ code for faster inference and deployment in various 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 ovachiever/droid-tings --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

ovachiever

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 ovachiever/droid-tings at 7acd12a754.

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

Commit
7acd12a754
Manifest
skills/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

The ovachiever-llama-cpp skill provides access to and utilizes the Llama.cpp inference engine. It allows AI agents to run large language models (LLMs) locally, offering improved privacy and potentially reduced latency compared to cloud-based solutions. This skill is particularly useful for tasks requiring offline processing or sensitive data handling.

When to use it

  • Offline LLM Inference: When internet connectivity is unreliable or unavailable, this skill enables the agent to continue using an LLM.
  • Privacy-Sensitive Tasks: Process confidential information locally without sending it to external servers.
  • Low-Latency Applications: Reduce response times by running models directly on the device.
  • Resource Constrained Environments: Execute LLMs on devices with limited computational resources, leveraging Llama.cpp's optimized performance.

Key capabilities

  • Local LLM execution via Llama.cpp
  • Improved privacy through offline processing
  • Potential for reduced latency
  • Optimized resource utilization

Example prompts

  • "Run the 'mistral-7b' model locally and summarize this document."
  • "Generate a poem using the 'llama-2-7b-chat' model, without an internet connection."
  • “Translate this paragraph into French using the local LLM.”

Tips & gotchas

The skill requires Llama.cpp to be installed on the system where the AI agent is running. Ensure sufficient RAM and storage are available for the selected language model as performance will depend heavily on hardware capabilities.