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BytesAgain β€Ί Skills β€Ί Prompt Evolution Engine
πŸ¦€ ClawHub

Prompt Evolution Engine

by @charlie-morrison

Iteratively improve AI prompts by analyzing, rewriting, comparing, and refining them using structured patterns for clarity, structure, and format compliance.

Versionv1.0.0
Downloads496
#writing#legal
TERMINAL
clawhub install prompt-evolution-engine

πŸ“– About This Skill

Prompt Optimizer

Iteratively improve AI prompts through structured evaluation, A/B testing, and feedback-driven refinement. Use when a prompt underperforms, produces inconsistent results, or needs optimization for a specific use case.

Usage

Optimize this prompt: [paste your prompt]

Or with context: 

Optimize this prompt for [goal]. Current issues: [problems]. Target model: [model name].

How It Works

1. Analyze β€” identify structural weaknesses (vague instructions, missing constraints, poor examples) 2. Rewrite β€” apply proven prompt engineering patterns (chain-of-thought, few-shot, role-setting, output format) 3. Compare β€” generate before/after evaluation with expected improvement areas 4. Iterate β€” if user provides feedback on the rewritten prompt, refine further

Optimization Patterns Applied

  • Clarity: Replace ambiguous language with specific, measurable instructions
  • Structure: Add section headers, numbered steps, output format templates
  • Constraints: Add boundaries (length, tone, forbidden patterns, edge cases)
  • Examples: Generate few-shot examples if missing
  • Chain-of-thought: Add reasoning steps for complex tasks
  • Role/persona: Set context-appropriate expertise framing
  • Output anchoring: Specify exact output format (JSON, markdown, etc.)

    • Parameters

    | Parameter | Description | Default | |-----------|-------------|---------| | goal | What the prompt should achieve | Inferred from content | | model | Target LLM (affects strategy) | General-purpose | | max_tokens | Target output length | No limit | | style | concise / detailed / creative | detailed | | iterations | How many refinement passes | 1 |

    Output Format

    ## Analysis
[Weaknesses identified in original prompt]
    Optimized Prompt
    
[The improved prompt, ready to copy-paste]
    Changes Made
    
[Bullet list of specific improvements and why]
    Expected Impact
    
[What should improve: consistency, accuracy, relevance, format compliance]

    Advanced Usage

    Batch Optimization

    Optimize these 3 prompts for the same task, pick the best approach:
1. [prompt A]
2. [prompt B]  
3. [prompt C]

    A/B Test Design

    Create an A/B test for this prompt. Generate variant A (structured) and variant B (conversational). Include 5 test inputs to compare.

    Model-Specific Tuning

    Optimize this prompt specifically for Claude Sonnet 4.6. Use extended thinking triggers and XML tags.

    πŸ’‘ Examples

    Optimize this prompt: [paste your prompt]

    Or with context: 

    Optimize this prompt for [goal]. Current issues: [problems]. Target model: [model name].