Prompt Engineering System
/prompt-improver using Google's whitepaper methodology. Transforms basic prompts into structured, effective ones.
About this system
Most AI prompts are ineffective—vague, missing context, poorly structured. The Prompt Engineering System transforms basic prompts into optimized versions using proven methodologies.
Based on Google's prompting whitepaper and real-world testing, the system applies:
- Clear role and task definition
- Structured output format specification
- Context and constraints
- Examples and anti-examples
- Step-by-step reasoning requests
The result: more consistent, higher-quality outputs with less iteration.
How to set it up
Learn the framework
Understand the components of effective prompts: role, task, format, context.
Install /prompt-improver
Add the skill to your Claude Code skills library.
Practice with examples
Run existing prompts through the improver to see the transformation.
Internalize patterns
Eventually you write better prompts naturally.
Workflow
Write basic prompt
Start with your initial, unoptimized prompt.
Run through improver
Use /prompt-improver to analyze and enhance.
Review suggestions
Understand what was improved and why.
Test both versions
Compare outputs from original and improved prompts.
Variations
Quick fix
Minor improvements to an almost-good prompt.
Full redesign
Complete restructuring of a poor prompt.
Template creation
Turn one-off prompts into reusable templates.
Sample prompt
Analyze this prompt and improve it using these principles:
1. Clear role definition
2. Specific task description
3. Output format specification
4. Relevant context/constraints
5. Examples if helpful
6. Anti-examples if helpful
Original prompt:
{{prompt}}
Provide the improved version with brief explanations.