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prompt-optimization

npx machina-cli add skill aiskillstore/marketplace/prompt-optimization --openclaw
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Prompt Optimization

This skill optimizes prompts for LLMs and AI systems, focusing on effective prompt patterns, few-shot learning, and optimal AI interactions.

When to Use This Skill

  • When building AI features or agents
  • When improving LLM response quality
  • When crafting system prompts
  • When optimizing agent performance
  • When implementing few-shot learning
  • When designing AI workflows

What This Skill Does

  1. Prompt Design: Creates effective prompts with clear structure
  2. Few-Shot Learning: Implements few-shot examples for better results
  3. Chain-of-Thought: Uses reasoning patterns for complex tasks
  4. Output Formatting: Specifies clear output formats
  5. Constraint Setting: Sets boundaries and constraints
  6. Performance Optimization: Improves prompt efficiency and results

How to Use

Optimize Prompt

Optimize this prompt for better results
Create a system prompt for a code review agent

Specific Patterns

Implement few-shot learning for this task

Prompt Techniques

Structure

Clear Sections:

  • Role definition
  • Task description
  • Constraints and boundaries
  • Output format
  • Examples

Few-Shot Learning

Pattern:

  • Provide 2-3 examples
  • Show input-output pairs
  • Demonstrate desired style
  • Include edge cases

Chain-of-Thought

Approach:

  • Break down complex tasks
  • Show reasoning steps
  • Encourage step-by-step thinking
  • Verify intermediate results

Examples

Example 1: Code Review Prompt

Input: Create optimized code review prompt

Output:

## Optimized Prompt: Code Review

### The Prompt

You are an expert code reviewer with 10+ years of experience.

Review the provided code focusing on:

  1. Security vulnerabilities
  2. Performance optimizations
  3. Code maintainability
  4. Best practices

For each issue found, provide:

  • Severity level (Critical/High/Medium/Low)
  • Specific line numbers
  • Explanation of the issue
  • Suggested fix with code example

Format your response as a structured report with clear sections.


### Techniques Used
- Role-playing for expertise
- Clear evaluation criteria
- Specific output format
- Actionable feedback requirements

Best Practices

Prompt Design

  1. Be Specific: Clear, unambiguous instructions
  2. Provide Examples: Show desired output format
  3. Set Constraints: Define boundaries clearly
  4. Iterate: Test and refine prompts
  5. Document: Keep track of effective patterns

Related Use Cases

  • AI agent development
  • LLM optimization
  • System prompt creation
  • Few-shot learning implementation
  • AI workflow design

Source

git clone https://github.com/aiskillstore/marketplace/blob/main/skills/89jobrien/prompt-optimization/SKILL.mdView on GitHub

Overview

Prompt Optimization tunes prompts for LLMs and AI systems to improve accuracy, reliability, and efficiency. It emphasizes clear structure, few-shot learning, chain-of-thought, and strict output formats to drive better results.

How This Skill Works

The skill teaches designing prompts with a clear structure: role, task, constraints, and output format; uses few-shot examples and chain-of-thought prompts to guide reasoning; and provides methods to test and refine prompts for performance.

When to Use It

  • Building AI features or agents
  • Improving LLM response quality
  • Crafting system prompts
  • Implementing few-shot learning
  • Designing AI workflows

Quick Start

  1. Step 1: Analyze the task and define the agent's role
  2. Step 2: Draft a structured prompt (Role, Task, Constraints, Output, Examples)
  3. Step 3: Add few-shot examples and test; iterate on feedback

Best Practices

  • Be Specific: give clear, unambiguous instructions
  • Provide Examples: show desired output format
  • Set Constraints: define boundaries and rules
  • Iterate: test prompts and refine them
  • Document: track effective patterns and changes

Example Use Cases

  • Optimized Code Review Prompt
  • System prompt for a code-reviewing agent
  • Few-shot task pattern with 2-3 examples
  • Chain-of-Thought prompt for complex tasks
  • Output formatting templates for structured responses

Frequently Asked Questions

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