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Prompt Engineering Patterns

Checked
wshobson·vlatest·ai-ml·MIT

Leverage proven prompt engineering techniques from wshobson to optimize AI model outputs and achieve desired results.

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 wshobson/agents --skill prompt-engineering-patterns -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

wshobson

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 wshobson/agents at 46891e7e60.

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

Commit
46891e7e60
Manifest
plugins/llm-application-dev/skills/prompt-engineering-patterns/SKILL.md
Scanned
10 files, 2,863 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 provides a collection of proven prompt engineering techniques and patterns that help users craft more effective prompts for AI agents. It includes strategies like role-playing, chain-of-thought reasoning, and iterative refinement to improve output quality and alignment with user goals.

When to use it

  • When you need to generate highly accurate or creative outputs from an AI agent.
  • When working on complex tasks that require multi-step reasoning or structured responses.
  • When training or fine-tuning prompts for consistent performance across different models.
  • When aiming to reduce ambiguity and improve the clarity of your instructions.

Key capabilities

  • Role-playing prompts to guide agents into specific personas or behaviors
  • Chain-of-thought prompting to encourage logical, step-by-step reasoning
  • Iterative refinement techniques to improve output quality through feedback loops
  • Prompt templates for common use cases like coding, writing, and analysis

Example prompts

  • "Act as a senior software engineer. Explain how to implement a REST API in Python."
  • "Walk me through the steps of solving this math problem logically."
  • "Refine this prompt: 'Write an article about climate change' — make it more specific and detailed."

Tips & gotchas

  • Start with simple prompts before applying complex patterns, as overcomplication can reduce effectiveness.
  • Be mindful of model limitations; not all patterns will work equally well across different AI agents or tasks.