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Llm Optimization

Checked
soborbo·vlatest·ai-ml

Helps with LLMs, optimization as part of building AI and machine learning applications workflows.

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 soborbo/claudeskills --skill llm-optimization --full-depth -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

soborbo

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 soborbo/claudeskills at 2b07d6a83b.

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

Commit
2b07d6a83b
Manifest
old/llm-optimization/SKILL.md
Scanned
5 files, 442 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 Large Language Model (LLM) performance. It focuses on improving response quality and reducing resource consumption by adjusting parameters like temperature, top_p, and frequency penalty. The skill aims to balance creativity with accuracy and efficiency in LLM outputs.

When to use it

  • Improving creative writing: Generate more diverse and engaging stories or poems while maintaining coherence.
  • Reducing hallucination: Minimize the generation of factually incorrect information by tightening response constraints.
  • Lowering API costs: Optimize prompts for fewer tokens, reducing expenses associated with LLM usage.
  • Ensuring consistent tone: Fine-tune parameters to maintain a specific writing style or brand voice across multiple responses.

Key capabilities

  • Parameter Adjustment (temperature, top_p, frequency penalty)
  • Response Quality Tuning
  • Resource Consumption Optimization
  • Hallucination Reduction

Example prompts

  • "Optimize this prompt for factual accuracy: [prompt]"
  • "Generate a creative story with a temperature of 0.7."
  • "Reduce the token count of this response while maintaining its meaning."

Tips & gotchas

The effectiveness of this skill depends on understanding how LLM parameters influence output. Experimentation and iterative refinement are key to achieving optimal results for specific use cases.