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Llm Prompt Optimizer

Checked
jmsktm·vlatest·ai-ml

Provides LLMs guidance and assistance for building AI and machine learning applications.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

Installs under a different name. Its SKILL.md names it LLM Prompt Optimizer, not the name it is listed under, so the command below uses that name.
  1. 1

    Run this in your project

    terminal
    $ npx skills add jmsktm/claude-settings --skill "LLM Prompt Optimizer" -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

jmsktm

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 jmsktm/claude-settings at c113b0d7e1.

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

Commit
c113b0d7e1
Manifest
skills/llm-prompt-optimizer/SKILL.md
Scanned
1 file, 146 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

The LLM Prompt Optimizer skill refines prompts provided to Large Language Models (LLMs) to improve response quality and relevance. It automatically adjusts prompt wording, structure, and context based on predefined rules and heuristics. The goal is to elicit more accurate, detailed, and useful outputs from the underlying language model.

When to use it

  • Improving creative writing: Enhance story generation or poem creation by optimizing prompts for better narrative flow and thematic consistency.
  • Refining code generation: Get cleaner and more functional code snippets by ensuring the prompt clearly defines requirements and constraints.
  • Boosting question answering accuracy: Optimize questions to reduce ambiguity and increase the likelihood of receiving precise and informative answers.
  • Generating structured data: Improve the formatting and completeness of generated lists, tables, or other structured outputs.

Key capabilities

  • Automated prompt refinement
  • Rule-based optimization heuristics
  • Contextual adjustment of prompts
  • Improved response quality

Example prompts

  • "Optimize this prompt: 'Write a short story about a cat.'"
  • "Refine the following code generation request: 'Generate Python function to calculate Fibonacci sequence.'"
  • "Improve this question for better accuracy: 'What is the capital of France?'"

Tips & gotchas

The skill's effectiveness depends on the clarity and specificity of the initial prompt. While it automates refinement, providing a well-defined starting point will yield the best results.