prompt-optimization
npx machina-cli add skill aiskillstore/marketplace/prompt-optimization --openclawPrompt 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
- Prompt Design: Creates effective prompts with clear structure
- Few-Shot Learning: Implements few-shot examples for better results
- Chain-of-Thought: Uses reasoning patterns for complex tasks
- Output Formatting: Specifies clear output formats
- Constraint Setting: Sets boundaries and constraints
- 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:
- Security vulnerabilities
- Performance optimizations
- Code maintainability
- 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
- Be Specific: Clear, unambiguous instructions
- Provide Examples: Show desired output format
- Set Constraints: Define boundaries clearly
- Iterate: Test and refine prompts
- 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
- Step 1: Analyze the task and define the agent's role
- Step 2: Draft a structured prompt (Role, Task, Constraints, Output, Examples)
- 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