Skip to content
TrustedSkillsbeta
Back to all skills

Foundation Models On Device

Checked
affaan-m·vlatest·security·MIT

This skill enables running powerful foundation models directly on your device for faster inference and enhanced privacy – ideal for offline AI tasks.

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 affaan-m/ecc --skill foundation-models-on-device -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

affaan-m

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 affaan-m/everything-claude-code at d3b8a3e908.

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

Commit
d3b8a3e908
Manifest
skills/foundation-models-on-device/SKILL.md
Scanned
3 files, 679 lines
Copies in repo
3 — all of them scanned
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 load and execute foundation models directly on local hardware rather than relying on cloud APIs. It ensures data privacy by keeping prompts, context, and generated outputs entirely within the user's device environment.

When to use it

  • Data Privacy Compliance: Process sensitive PII, legal documents, or proprietary code without transmitting data to external servers.
  • Offline Functionality: Maintain agent capabilities in environments with no internet connectivity or restricted network access.
  • Cost Reduction: Eliminate per-token API usage fees associated with cloud-based inference services.
  • Latency Optimization: Reduce response times by removing network round-trips for model inference.

Key capabilities

  • Local execution of foundation models on user devices.
  • Complete data isolation from external networks.
  • Direct integration within the agent's runtime environment.

Example prompts

  • "Run a sentiment analysis on this confidential client email using a locally hosted model."
  • "Summarize the attached internal strategy document without sending it to any cloud service."
  • "Debug this Python script using a small language model running on my laptop."

Tips & gotchas

Ensure your device meets the hardware requirements for the specific foundation model size you intend to run, as local inference is resource-intensive. Model performance may vary based on available RAM and CPU/GPU capabilities compared to optimized cloud servers.