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Prompt Engineering

Checked
martinholovsky·vlatest·ai-ml·Unlicense

Crafts optimized prompts for AI models to elicit desired responses, boosting accuracy and effectiveness in your 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 martinholovsky/claude-skills-generator --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

martinholovsky

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 martinholovsky/claude-skills-generator at 1086ef2567.

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

Commit
1086ef2567
Manifest
skills/prompt-engineering/SKILL.md
Scanned
4 files, 1,270 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

The prompt-engineering skill transforms raw user input into optimized, structured prompts tailored for AI agents. It automatically refines context, constraints, and formatting to ensure higher quality outputs from large language models.

When to use it

  • You need to generate complex code snippets with specific architectural requirements without writing verbose instructions.
  • You want to analyze unstructured text data (like legal contracts or research papers) for precise extraction of key entities.
  • You require consistent formatting in creative writing tasks, such as generating blog posts that adhere to a strict style guide.
  • You are debugging AI responses by providing the model with clearer, more defined system instructions.

Key capabilities

  • Context Optimization: Automatically expands brief queries into detailed prompts with necessary background information.
  • Constraint Enforcement: Adds specific rules and limitations to prevent hallucinations or off-topic answers.
  • Role Definition: Assigns expert personas to the AI agent to tailor the tone and depth of the response.
  • Format Structuring: Enforces output formats like JSON, Markdown tables, or step-by-step lists based on user needs.

Example prompts

  • "Optimize this prompt for a senior Python developer: 'Write a script to scrape data.'"
  • "Refine my request to extract customer sentiment from support tickets into a CSV format."
  • "Turn this vague idea into a detailed prompt for an AI copywriter to draft a marketing email."

Tips & gotchas

Ensure your initial input contains the core intent; the skill adds the necessary structure but cannot invent the fundamental goal. For best results, provide examples of the desired output format if you have specific structural requirements beyond standard text.