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Grepai Embeddings Ollama

Listed
yoanbernabeu·vlatest·ai-ml·MIT

Generates high-quality embeddings using GrepAI and Ollama for semantic search, similarity analysis, and knowledge graph construction.

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-embeddings-ollama -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 2 hosts that are neither well-known infrastructure nor declared in the skill's frontmatter: 192.168.1.100, 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.comundeclared192.168.1.100undeclared

    What it found

    • skills/embeddings/grepai-embeddings-ollama/SKILL.md:36contacts ollama.com
      curl -fsSL https://ollama.com/install.sh | sh  # Linux
    • skills/embeddings/grepai-embeddings-ollama/SKILL.md:63contacts 192.168.1.100
      endpoint: http://192.168.1.100:11434  # Remote Ollama server

  • 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/embeddings/grepai-embeddings-ollama/SKILL.md:36remote script piped into a shell
      curl -fsSL https://ollama.com/install.sh | sh  # Linux

  • 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/embeddings/grepai-embeddings-ollama/SKILL.md
Scanned
1 file, 295 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 enables AI agents to generate text embeddings using Ollama, a local large language model server. It allows agents to transform raw text into numerical vectors for semantic search and similarity analysis without relying on external cloud APIs.

When to use it

  • Local Privacy Compliance: When processing sensitive data that must remain entirely within your private infrastructure.
  • Cost Optimization: For high-volume embedding tasks where avoiding per-token API charges is critical.
  • Custom Model Flexibility: When you need to leverage specific open-source models hosted locally via Ollama rather than defaulting to proprietary services.
  • Offline Capabilities: In environments with restricted internet access where cloud-based vectorization is impossible.

Key capabilities

  • Direct integration with the Ollama inference engine for vector generation.
  • Support for various open-source embedding models hosted on local servers.
  • Enables semantic search and clustering workflows entirely offline.
  • Eliminates dependency on third-party API keys or external network connectivity.

Example prompts

  • "Generate embeddings for this batch of customer support tickets using the local Ollama model."
  • "Convert these product descriptions into vectors so I can perform a similarity search against my local database."
  • "Run an embedding task on this legal document to identify key semantic concepts without sending data outside."

Tips & gotchas

Ensure your local machine has sufficient RAM and CPU resources, as running embedding models locally is resource-intensive. Verify that Ollama is correctly configured and the desired model is pulled before attempting to generate embeddings through this skill.