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Llama Cpp

Checked
orchestra-research·vlatest·ai-ml·MIT

LlamaCpp enables running Meta’s LLaMA language model locally using C++, offering efficient inference and privacy without internet dependency.

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

orchestra-research

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 orchestra-research/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 allows AI agents to leverage the llama.cpp library for running large language models locally. It provides capabilities for efficient inference, quantization support, and compatibility with various hardware configurations. The agent can utilize this skill to process text data and generate responses without relying on external APIs or cloud services.

When to use it

  • Offline Processing: When internet connectivity is unavailable, the agent needs to analyze documents or respond to prompts.
  • Privacy-Sensitive Tasks: For scenarios requiring local data processing and preventing information from leaving a secure environment.
  • Resource-Constrained Environments: To deploy language models on devices with limited computational resources.
  • Custom Model Integration: When you need to use specific, locally stored LLM models not accessible through standard APIs.

Key capabilities

  • llama.cpp library integration
  • Local Large Language Model (LLM) inference
  • Quantization support for efficient resource usage
  • Hardware compatibility across various configurations

Example prompts

  • "Analyze this document and summarize the key findings." (followed by providing a text file)
  • "Generate a creative story based on these keywords: [keyword1], [keyword2], [keyword3]."
  • "Translate this paragraph into French:" (followed by providing a paragraph of text).

Tips & gotchas

  • Ensure the necessary llama.cpp dependencies are installed and configured correctly before using this skill.
  • Performance will depend on available hardware resources; quantization can help optimize for lower-powered devices.