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Prompt Engineering

Listed
ancoleman·vlatest·ai-ml·MIT

This skill teaches you how to craft effective prompts for AI models, maximizing their output quality and usefulness for your specific needs.

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 ancoleman/ai-design-components --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

ancoleman

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 failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • No undeclared network calls: failed

    Contacts 1 host that is neither well-known infrastructure nor declared in the skill's frontmatter: upload.wikimedia.org.

    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.

    Hosts contacted

    upload.wikimedia.orgundeclared

    What it found

    • skills/prompt-engineering/examples/openai-examples.py:492contacts upload.wikimedia.org
      image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/320px-Gfp-wisconsin-madison-the-nature-bo…

  • 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 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 ancoleman/ai-design-components at 76551b7b19.

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

Commit
76551b7b19
Manifest
skills/prompt-engineering/SKILL.md
Scanned
15 files, 8,569 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 allows AI agents to craft effective prompts for various language models. It focuses on generating clear, concise instructions that elicit desired responses, improving the quality and relevance of outputs from those models. The skill aims to enhance agent performance by optimizing communication with underlying LLMs.

When to use it

  • Generating creative content: When you need an AI agent to write stories, poems, or scripts, this skill can help structure prompts for better results.
  • Extracting specific information: If you want an agent to summarize a document or answer questions from text, well-crafted prompts will improve accuracy.
  • Improving code generation: When requesting code snippets, the skill helps formulate instructions that lead to more functional and correct code.
  • Refining chatbot responses: To ensure chatbots provide helpful and relevant answers, this skill can optimize their prompt engineering capabilities.

Key capabilities

  • Prompt construction
  • Instruction refinement
  • Output elicitation
  • LLM communication optimization

Example prompts

  • "Write a short story about a cat who learns to code."
  • "Summarize the key findings in this research paper: [paste text here]"
  • "Generate Python code for a simple web scraper."

Tips & gotchas

The effectiveness of this skill depends on the underlying language model's capabilities. Complex or nuanced tasks may require iterative prompt refinement to achieve optimal results.