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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.

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SKILL.md（原文）

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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

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

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概要と使いどころ

acquisition-policy-workflow

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Trigger for: explaining current FAR, DFARS, or agency-supplement text; determining documented acquisition-policy status for an agency and FAR part; comparing codified text, RFO model text, and agency deviations; tracing acquisition rulemaking; finding procurement comment periods; analyzing public comments; refreshing prior policy analysis; or producing an Acquisition Policy Impact Brief. Use published federal sources and supplied policy documents without making legal or procurement-specific applicability determinations. Do not use for clause selection, market research execution, opportunities, pricing, grants, or cooperative agreements.

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