prompt-optimization
Expert prompt optimization for LLMs and AI systems. Use when building AI features, improving agent performance, crafting system prompts, or optimizing LLM interactions. Masters prompt patterns and techniques.
安装与使用
请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求,再添加到工作流。
查看来源 ↗使用指南技能说明
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
- 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
使用示例
查看来源仓库中的使用示例与配置说明。
查看来源 ↗来源历史
- 收录时间
- 2026-01-20
- 索引更新时间
- 2026-01-28
- Branch
- main
收录与同步日期属于本站记录。源码修改历史请查看来源仓库。
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