The Prompt Engineer skill helps you craft, refine, and optimize prompts for Claude Code and other AI systems. It applies proven prompt engineering principles including clarity, specificity, context provision, and structural best practices to transform vague requests into effective AI instructions.
This skill analyzes existing prompts for weaknesses, suggests improvements based on prompt engineering research, and helps you build prompt libraries for recurring tasks. It's particularly valuable when you need consistent, high-quality AI outputs or want to maximize the effectiveness of complex multi-step AI workflows.
Whether you're creating one-off prompts or building reusable templates, this skill ensures your AI interactions are clear, actionable, and produce the results you need.
Core Workflows
Workflow 1: Analyze & Optimize Existing Prompt
Receive
the current prompt from user
Analyze
against prompt engineering principles:
Clarity: Is the request unambiguous?
Specificity: Are outputs well-defined?
Context: Is necessary background provided?
Structure: Is the prompt well-organized?
Constraints: Are limitations clearly stated?
Identify
weaknesses and improvement opportunities
Provide
optimized version with explanations
Test
improved prompt if requested
Iterate
based on results
Workflow 2: Design New Prompt from Scratch
Clarify
the goal: What outcome is needed?
Gather
requirements:
Target AI system capabilities
Output format requirements
Domain context needed
Edge cases to handle
Structure
the prompt using proven patterns:
Role/persona if beneficial
Clear task description
Specific constraints and requirements
Output format specification
Examples if complex
Draft
initial version
Refine
for clarity and completeness
Document
usage guidelines
Workflow 3: Build Prompt Template Library
Identify
recurring prompt patterns in workflow
Extract
reusable components
Parameterize
variable elements
Document
template with:
Purpose and use cases
Parameter descriptions
Example usage
Expected outputs
Test
template with multiple scenarios
Store
in organized library structure
Quick Reference
Action
Command/Trigger
Optimize existing prompt
"Optimize this prompt: [prompt]"
Design new prompt
"Design a prompt for [goal]"
Review prompt quality
"Review this prompt: [prompt]"
Create template
"Create a prompt template for [use case]"
Apply best practices
"Apply prompt engineering best practices to [prompt]"
Fix prompt issues
"This prompt isn't working well: [prompt]"
Best Practices
Be Specific
Replace vague terms with concrete requirements
Bad: "Make it better"
Good: "Increase response accuracy by providing 3 cited examples"
Example: "For a technical audience familiar with React..."
Structure Clearly
Use formatting to organize complex prompts
Sections, bullets, numbered steps
Clear delineation between instructions and examples
Define Success
Specify what good output looks like
Format requirements (JSON, markdown, etc.)
Length constraints
Quality criteria
Use Examples
Show don't just tell for complex outputs
Provide 1-3 examples of desired output
Include edge cases if relevant
Iterate
Prompts improve through testing
Start simple, add complexity as needed
Test with edge cases
Refine based on actual outputs
Separate Concerns
Don't mix multiple requests
One clear goal per prompt
Chain prompts for multi-step workflows
Constrain Appropriately
Set boundaries without over-constraining
Specify limits (word count, format)
Allow flexibility where creativity helps
Advanced Techniques
Chain-of-Thought Prompting
Encourage step-by-step reasoning by asking AI to "think through" problems:
Before providing the final answer, work through:
1. What are the key factors?
2. What are the trade-offs?
3. What does the evidence suggest?
Then provide your conclusion.
Few-Shot Learning
Provide examples of input-output pairs:
Example 1: [input] → [output]
Example 2: [input] → [output]
Now apply the same pattern to: [new input]
Role-Based Prompting
Assign expertise or perspective:
As a senior React architect with 10 years of experience,
review this component for performance issues...
Constraint-Based Refinement
Use specific constraints to shape output:
Requirements:
- Maximum 3 paragraphs
- Include code examples
- Cite sources
- Use beginner-friendly language
Common Pitfalls to Avoid
Assuming context the AI doesn't have
Being too vague about desired output format
Mixing multiple unrelated requests
Over-complicating simple requests
Not specifying constraints until after receiving output
Forgetting to provide examples for complex patterns
Using ambiguous language or jargon without definition