Skip to content
TrustedSkillsbeta
Back to all skills

Grepai Ollama Setup

Listed
yoanbernabeu·vlatest·ai-ml·MIT

Automates greptime setup using Ollama containers for local time series data analysis and visualization.

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 yoanbernabeu/grepai-skills --skill grepai-ollama-setup -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

yoanbernabeu

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 failed 2 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • No undeclared network calls: failed

    Contacts 1 host that is neither well-known infrastructure nor declared in the skill's frontmatter: ollama.com.

    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.

    Hosts contacted

    ollama.comundeclared

    What it found

    • skills/getting-started/grepai-ollama-setup/SKILL.md:48contacts ollama.com
      curl -fsSL https://ollama.com/install.sh | sh

  • No pipe-to-shell installers: failed

    Runs code fetched at install time in 1 place.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

    What it found

    • skills/getting-started/grepai-ollama-setup/SKILL.md:48remote script piped into a shell
      curl -fsSL https://ollama.com/install.sh | sh

  • 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 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.

  • Pinned to a commit: passed

    Scanned yoanbernabeu/grepai-skills at 382d40261c.

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

Commit
382d40261c
Manifest
skills/getting-started/grepai-ollama-setup/SKILL.md
Scanned
1 file, 204 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 automates the setup of Ollama, a framework for running large language models locally. It handles downloading and configuring necessary components, allowing AI agents to easily access and utilize powerful LLMs without relying on external APIs. The skill simplifies the process of getting started with local LLM inference, making it accessible even for users without extensive technical expertise.

When to use it

  • Local LLM experimentation: You want your agent to test different language models locally without incurring API costs or dealing with rate limits.
  • Offline AI applications: Your application requires offline functionality and cannot rely on internet connectivity for LLM inference.
  • Privacy-focused workflows: You need to process sensitive data using a local LLM, ensuring that information remains within your environment.
  • Custom model integration: You want to use a specific or custom-trained language model not readily available through standard APIs.

Key capabilities

  • Automated Ollama installation and configuration
  • Model downloading from Hugging Face Hub
  • Simplified local LLM inference setup
  • Support for various language models compatible with Ollama

Example prompts

  • "Install Ollama and download the Llama2 7B model."
  • "Set up Ollama and use Mistral as my default model."
  • “Configure Ollama to run on port 8000.”

Tips & gotchas

  • Ensure you have sufficient disk space for downloading language models, which can be several gigabytes in size.
  • Ollama requires a compatible operating system (currently macOS and Linux).