Does this skill work with all Claude models?

Yes, while the magnitude of improvement can vary across model versions, these research-backed linguistic patterns generally improve performance across all modern LLMs.

What kind of improvements can I expect?

Research from institutions like MBZUAI and Google DeepMind indicates quality improvements ranging from 45% to over 115% depending on task difficulty.

Why is self-evaluation included in this skill?

Self-evaluation forces the AI to calibrate its own output, identifying hidden assumptions and potential risks before you implement the code.

How does incentive prompting actually work with an AI?

It leverages statistical pattern-matching by using language associated with high-effort human training examples, triggering the model to select from higher-quality text distributions.

When should I use the /ai-eng/optimize command?

Use it whenever you are dealing with complex technical problems, critical production code changes, or when you need to ensure an agent follows a rigorous logical path.

Incentive Prompting Optimizer

Name: Incentive Prompting Optimizer
Author: v1truv1us

byv1truv1us

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数据科学与机器学习

Optimizes AI outputs using research-backed prompting techniques to increase response quality and accuracy by up to 115%.

关于

The Incentive Prompting skill implements scientifically-proven strategies—including monetary incentive framing, step-by-step reasoning, and expert persona assignment—to significantly enhance the reliability of AI-generated solutions. By leveraging statistical pattern-matching found in high-quality training data, it helps developers elicit expert-level performance from Claude for complex technical challenges, debugging, and architectural planning. This skill is essential for high-stakes tasks where precision is critical and standard prompting falls short.

主要功能

2 GitHub stars
Step-by-step reasoning triggers to improve logical accuracy from 34% to 80%
Automated expert persona generation for deep domain-specific expertise
Challenge framing and stakes language to elicit higher-effort responses
Research-backed prompting templates based on Google DeepMind and MBZUAI studies
Self-evaluation mechanisms with confidence ratings and risk assessment

使用场景

Solving high-stakes debugging or complex architectural problems
Optimizing agent instructions for consistent, production-grade output
Refining multi-step technical implementations where logic is brittle

关于

主要功能

2 GitHub stars
Step-by-step reasoning triggers to improve logical accuracy from 34% to 80%
Automated expert persona generation for deep domain-specific expertise
Challenge framing and stakes language to elicit higher-effort responses
Research-backed prompting templates based on Google DeepMind and MBZUAI studies
Self-evaluation mechanisms with confidence ratings and risk assessment

使用场景

Solving high-stakes debugging or complex architectural problems
Optimizing agent instructions for consistent, production-grade output
Refining multi-step technical implementations where logic is brittle