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Prompt Optimization Patterns

Checked
ikramahahmed·vlatest·ai-ml·MIT

Helps with optimization, patterns as part of building AI and machine learning applications workflows.

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 ikramahahmed/prompt-optimization-patterns --skill prompt-optimization-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

ikramahahmed

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Install target

Live repository

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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.

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    Makes no network calls.

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  • Pinned to a commit: passed

    Scanned ikramahahmed/prompt-optimization-patterns at 408a43b5e1.

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Commit
408a43b5e1
Manifest
SKILL.md
Scanned
3 files, 752 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 refine prompts to achieve more predictable and desirable outputs from AI agents. It provides a library of prompt optimization patterns, allowing users to systematically improve prompt structure and clarity. The goal is to reduce ambiguity and increase the likelihood of receiving accurate, relevant, and high-quality responses.

When to use it

  • Improving response quality: When initial AI agent responses are inconsistent or unsatisfactory.
  • Reducing hallucinations: When the AI agent frequently generates inaccurate or fabricated information.
  • Controlling output format: To ensure the AI agent consistently delivers results in a specific structure (e.g., lists, tables, code).
  • Complex tasks: When breaking down complex requests into smaller, more manageable steps for the AI agent.

Key capabilities

  • Library of prompt optimization patterns
  • Systematic approach to prompt refinement
  • Improved response consistency and accuracy
  • Control over output format

Example prompts

  • "Apply the 'Chain-of-Thought' pattern to this prompt: [original prompt]"
  • "Suggest improvements using the 'Role Prompting' pattern for this task: [task description]"
  • "Refine this prompt using the 'Few-Shot Learning' approach: [prompt]"

Tips & gotchas

The effectiveness of these patterns depends on understanding the underlying AI model’s capabilities and limitations. Experimentation is key to finding the optimal combination of patterns for a given task.