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Parameter Optimization

Checked
heshamfs·vlatest·ai-ml·Apache-2.0

Automatically refines model parameters to maximize performance based on provided data and objectives.

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 heshamfs/materials-simulation-skills --skill parameter-optimization -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

heshamfs

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 heshamfs/materials-simulation-skills at fa1ce8de76.

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

Commit
fa1ce8de76
Manifest
skills/simulation-workflow/parameter-optimization/SKILL.md
Scanned
11 files, 1,821 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 optimizes parameters for materials simulations. It automatically adjusts input variables to improve the accuracy and efficiency of these complex calculations. The goal is to minimize error and reduce computational time while maintaining reliable results.

When to use it

  • Fine-tuning a simulation model where initial parameter guesses are inaccurate.
  • Reducing the runtime of computationally expensive materials simulations.
  • Exploring a wide range of parameters quickly to identify optimal settings for specific material properties.
  • Automating the process of finding best practices for complex material models.

Key capabilities

  • Parameter adjustment
  • Error minimization
  • Runtime reduction
  • Automation

Example prompts

  • "Optimize the parameters for my DFT simulation of silicon."
  • "Can you find the fastest settings to calculate the elastic modulus of aluminum?"
  • "Improve the accuracy of this molecular dynamics simulation by adjusting these parameters: timestep, temperature, and potential function."

Tips & gotchas

The skill requires a well-defined materials simulation workflow as input. Successful optimization depends on having clear metrics for evaluating simulation performance (e.g., error rate, runtime).