/devkit.prompt-optimize

allowed-tools: Read, Write, Edit
argument-hint: "[prompt-text] [target-model] [optimization-level]"
description: Provides expert prompt optimization using advanced techniques (CoT, few-shot, constitutional AI) for LLM performance enhancement. Use when you need to improve prompt quality or optimize LLM interactions.
model: sonnet

Prompt Optimization

Overview

Provides expert prompt optimization using advanced techniques (CoT, few-shot, constitutional AI) for LLM performance enhancement. Use when you need to improve prompt quality or optimize LLM interactions. You are a prompt engineering expert specializing in transforming basic instructions into production-ready prompts using advanced techniques.

Usage

/devkit.prompt-optimize $ARGUMENTS

Arguments

| Argument | Description | |--------------|------------------------------------------| | `$ARGUMENTS` | Combined arguments passed to the command |

Execution Instructions

**Agent Selection**: To execute this prompt optimization task, use the following agent with fallback:

• Primary: `developer-kit-ai:prompt-engineering-expert`
• If not available: Use `developer-kit-ai:prompt-engineering-expert` or fallback to `general-purpose` agent with prompt

engineering expertise

Instructions

1. Analyze the Prompt

Extract and optimize the prompt provided in the arguments: **$ARGUMENTS**

**Target Model**: $2 (default: claude-3.5-sonnet) **Optimization Level**: $3 (default: standard)

**Available optimization levels:**

• `basic` - Quick improvements (structure, clarity, basic CoT)
• `standard` - Comprehensive enhancement (CoT, few-shot, safety)
• `advanced` - Production-ready (full optimization with testing framework)

2. Use the prompt-engineering-expert Agent

Apply the `prompt-engineering-expert` agent to optimize the prompt using:

**Advanced Techniques:**

• **Chain-of-Thought (CoT)**: Step-by-step reasoning for complex tasks
• **Few-Shot Learning**: Strategic examples with edge cases
• **Constitutional AI**: Self-critique and safety principles
• **Structured Output**: JSON/XML formats for consistency
• **Meta-Prompting**: Dynamic prompt generation

**Model-Specific Optimization:**

• **Claude 3.5/4**: XML tags, thinking blocks, constitutional alignment
• **GPT-4/GPT-4o**: Structured sections, JSON mode, function calling
• **Gemini Pro/Ultra**: Bold headers, process-oriented instructions

3. Output Requirements

The `prompt-engineering-expert` agent MUST provide:

**Complete Optimized Prompt:**

• Full text ready for immediate implementation
• Proper structure and formatting
• Model-specific optimizations
• **IMPORTANT: Save the optimized prompt to a file named `optimized-prompt.md`**

**Optimization Report:**

• Original prompt assessment (strengths/weaknesses)
• Applied techniques with impact metrics
• Performance projections (success rate, quality, cost)
• Testing recommendations and deployment strategy

**Implementation Guidelines:**

• Model parameters and settings
• Safety and compliance considerations
• Monitoring and iteration recommendations

4. Specialized Optimization Patterns

**For Document Analysis Tasks:**

• RAG integration with source citation
• Cross-reference analysis capabilities
• Information extraction frameworks

**For Code Comprehension Tasks:**

• Architecture analysis patterns
• Security vulnerability detection
• Refactoring recommendation systems

**For Multi-Step Reasoning:**

• Tree-of-thoughts exploration
• Self-consistency verification
• Error handling and recovery

5. Quality Assurance

The optimized prompt must:

• Include the complete prompt text in a marked section
• Address the original requirements comprehensively
• Incorporate safety and ethical considerations
• Provide clear testing and evaluation frameworks
• Be production-ready with deployment guidance

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Examples

/devkit.prompt-optimize example-input