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Prompt Engineer

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

This Prompt Engineer skill helps craft optimized prompts for AI models, boosting response quality and achieving desired outputs effectively.

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 akiselev/qt-llm-template --skill prompt-engineer -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

akiselev

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 akiselev/qt-llm-template at 6855e82e0c.

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

Commit
6855e82e0c
Manifest
.claude/skills/prompt-engineer/SKILL.md
Scanned
60 files, 20,544 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, akiselev-prompt-engineer, provides tools to refine and optimize prompts for large language models (LLMs). It allows users to iteratively improve LLM outputs by suggesting modifications and evaluating different prompt variations. The core functionality focuses on enhancing the quality and relevance of responses generated by LLMs.

When to use it

  • Improving Response Quality: When initial LLM responses are unsatisfactory, vague, or inaccurate.
  • Experimenting with Prompt Strategies: To test different phrasing and approaches for eliciting desired behaviors from an LLM.
  • Refining Complex Tasks: When breaking down a complex task into smaller steps and crafting prompts for each step requires precision.
  • Debugging Unexpected Behavior: When an LLM exhibits undesirable or inconsistent behavior, prompt engineering can help identify and correct the underlying issues.

Key capabilities

  • Prompt suggestion & modification
  • Evaluation of different prompt variations
  • Iterative refinement process

Example prompts

  • "Can you suggest a better way to phrase this prompt: 'Write a short story about a cat'?"
  • "Evaluate these two prompts for generating marketing copy: [prompt 1], [prompt 2]"
  • "I want the LLM to summarize this article. Suggest some improvements to my current prompt."

Tips & gotchas

This skill is most effective when you have a clear understanding of the desired outcome from the LLM and can provide specific examples of what works or doesn't work with initial prompts. The quality of suggestions depends on the clarity of your feedback during the iterative refinement process.