Prompt Optimization
- 42 installs
- 4 repo stars
- Updated April 11, 2026
- 89jobrien/steve
prompt-optimization is a Claude Code skill that optimizes prompts for LLMs using patterns like few-shot learning, chain-of-thought, output formatting, and constraint setting.
About
prompt-optimization is a Claude Code skill for improving prompts for LLMs and AI systems. It covers prompt structure, few-shot learning, chain-of-thought reasoning, output formatting, and constraint setting. A developer uses it when building AI features or agents, crafting system prompts, or trying to raise LLM response quality.
- Prompt patterns: few-shot, chain-of-thought, output formatting, constraints
- For system prompts and agent-performance improvement
- Marked status: unpublished in frontmatter
Prompt Optimization by the numbers
- 42 all-time installs (skills.sh)
- Ranked #8,023 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
- Data as of Jul 28, 2026 (Skillselion catalog sync)
prompt-optimization capabilities & compatibility
- Capabilities
- prompt optimization · system prompt design · few shot prompting · chain of thought
- Pricing
- Free
What prompt-optimization says it does
This skill optimizes prompts for LLMs and AI systems, focusing on effective prompt patterns, few-shot learning, and optimal AI interactions.
Chain-of-Thought
npx skills add https://github.com/89jobrien/steve --skill prompt-optimizationAdd your badge
Show developers this skill is listed on Skillselion. Paste this into your README.
| Installs | 42 |
|---|---|
| repo stars | ★ 4 |
| Last updated | April 11, 2026 |
| Repository | 89jobrien/steve ↗ |
What it does
Rewrite or design a system/task prompt for an LLM using few-shot and chain-of-thought patterns.
Who is it for?
Crafting system prompts, improving LLM response quality, and implementing few-shot or chain-of-thought patterns.
Skip if: Non-LLM tasks; it does not build application code, only shapes prompts.
When should I use this skill?
Building AI features or agents, writing a system prompt, or optimizing an LLM interaction.
What you get
Prompts gain clear role, task, constraints, output format, and examples for more reliable LLM responses.
By the numbers
- 6 techniques (prompt design, few-shot, chain-of-thought, output formatting, constraints, performance)
Files
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
1. Prompt Design: Creates effective prompts with clear structure 2. Few-Shot Learning: Implements few-shot examples for better results 3. Chain-of-Thought: Uses reasoning patterns for complex tasks 4. Output Formatting: Specifies clear output formats 5. Constraint Setting: Sets boundaries and constraints 6. Performance Optimization: Improves prompt efficiency and results
How to Use
Optimize Prompt
Optimize this prompt for better resultsCreate a system prompt for a code review agentSpecific Patterns
Implement few-shot learning for this taskPrompt 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 PromptYou are an expert code reviewer with 10+ years of experience.
Review the provided code focusing on:
1. Security vulnerabilities 2. Performance optimizations 3. Code maintainability 4. 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 requirementsBest Practices
Prompt Design
1. Be Specific: Clear, unambiguous instructions 2. Provide Examples: Show desired output format 3. Set Constraints: Define boundaries clearly 4. Iterate: Test and refine prompts 5. 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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What prompt techniques does it use?
It applies clear structure (role, task, constraints, output format, examples), few-shot learning, and chain-of-thought reasoning.
When should I use it?
When building AI features or agents, crafting system prompts, or optimizing LLM interactions.