Skip to content
TrustedSkillsbeta
Back to all skills

Hqq Quantization

Checked
davila7·vlatest·ai-ml·MIT

Hqq Quantization optimizes model size & speed by reducing numerical precision, making AI faster and more efficient for deployment.

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 davila7/claude-code-templates --skill hqq-quantization -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

davila7

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 davila7/claude-code-templates at 79182c51fb.

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

Commit
79182c51fb
Manifest
cli-tool/components/skills/ai-research/optimization-hqq/SKILL.md
Scanned
3 files, 1,345 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, hqq-quantization, enables AI agents to reduce the precision of numerical data (quantization). This process can significantly decrease model size and improve inference speed, particularly beneficial for deployment on resource-constrained devices. It supports various quantization schemes and provides tools for evaluating the impact on accuracy.

When to use it

  • Deploying models to edge devices: Reduce model size for mobile phones or embedded systems with limited memory.
  • Accelerating inference: Speed up predictions by using lower precision data types.
  • Reducing storage costs: Lower the storage footprint of large language models.
  • Optimizing model performance: Balance accuracy and efficiency when deploying in real-time applications.

Key capabilities

  • Quantization schemes (details not specified)
  • Accuracy evaluation tools
  • Model size reduction
  • Inference speed optimization

Example prompts

  • "Quantize this model using 8-bit integer precision."
  • "Evaluate the accuracy impact of quantizing my model to INT4."
  • "Reduce the size of this large language model for deployment on a Raspberry Pi."

Tips & gotchas

The effectiveness of quantization depends heavily on the specific model architecture and dataset. It's crucial to carefully evaluate the trade-off between reduced precision and potential accuracy loss after applying this skill.