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Prompt Engineering

Checked
sabahattinkalkan·vlatest·ai-ml·MIT

Crafts optimized prompts for AI models to elicit desired responses, boosting accuracy and effectiveness in various applications.

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 sabahattink/antigravity-fullstack-hq --skill prompt-engineering -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

sabahattinkalkan

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

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    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned sabahattinkalkan/antigravity-fullstack-hq at 90524b3f8e.

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

Commit
90524b3f8e
Manifest
skills/prompt-engineering/SKILL.md
Scanned
1 file, 115 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 helps AI agents craft more effective prompts. It enables users to refine instructions for language models, leading to improved output quality and accuracy. The tool focuses on generating better results from large language models (LLMs) through prompt optimization techniques.

When to use it

  • Generating creative content: Use this when you need the LLM to write stories, poems, or scripts that are engaging and well-structured.
  • Improving code generation: Employ this skill when requesting code snippets to ensure they're syntactically correct and functionally sound.
  • Extracting specific information: Leverage it for tasks like summarizing documents or answering questions where precise answers are crucial.
  • Refining complex instructions: Use the tool to break down complicated requests into smaller, more manageable prompts for better LLM comprehension.

Key capabilities

  • Prompt optimization
  • LLM instruction refinement
  • Creative content generation support
  • Code generation improvement

Example prompts

  • "Optimize this prompt for generating a short story: 'Write a story about a cat.'"
  • "Refine the following code generation request: 'Create a Python function to calculate Fibonacci numbers.'"
  • "Improve this instruction for summarizing a document: 'Summarize this article.'"

Tips & gotchas

This skill requires a basic understanding of how LLMs work and the types of prompts they respond well to. Experimentation with different prompt structures is key to achieving optimal results.