Will this skill change the original intent of my prompt?

No, the skill follows a strict rule never to change the original meaning or add unrequested features; its only goal is to make the existing intent clearer for the AI.

What is included in the Optimization Report?

The report includes a scoring matrix across four criteria (Clarity, Structure, Completeness, Guardrails), a list of identified issues, a table of specific changes, and the final optimized prompt text.

How does the Prompt Optimization skill improve AI performance?

It applies a structured analysis checklist to identify ambiguities, adds necessary context, and implements specific guardrails that prevent common AI errors and hallucinations.

Can I use this for complex multi-step instructions?

Yes, the skill is specifically designed to handle complex logic by adding chain-of-thought reasoning and clear hierarchical structures to long-form prompts.

Prompt Optimization

Name: Prompt Optimization
Author: fusengine

byfusengine

•

Productivity & Workflow

Refines and enhances existing AI prompts through structured analysis, clarity improvements, and robust guardrail implementation to maximize performance.

About

The Prompt Optimization skill provides a systematic framework for auditing and upgrading AI prompts, transforming vague or inconsistent instructions into high-performance directives. By following a rigorous five-step workflow—analysis, identification, correction, validation, and documentation—it addresses common issues like ambiguous language, lack of context, and undefined output formats. This tool is essential for developers looking to implement chain-of-thought reasoning, strengthen security constraints, and ensure predictable, production-grade results from their LLM interactions.

Key Features

Comparative reporting with detailed before/after performance scoring
Implementation of chain-of-thought reasoning and few-shot examples
Automated error handling and edge case integration
Structured 5-step optimization workflow (Analyze to Document)
Comprehensive audit of clarity, structure, and completeness
1 GitHub stars

Use Cases

Converting simple chat instructions into production-ready system prompts
Refining legacy prompts that produce inconsistent or low-quality AI outputs
Hardening prompt security by adding strict negative constraints and guardrails

About

Key Features

Comparative reporting with detailed before/after performance scoring
Implementation of chain-of-thought reasoning and few-shot examples
Automated error handling and edge case integration
Structured 5-step optimization workflow (Analyze to Document)
Comprehensive audit of clarity, structure, and completeness
1 GitHub stars

Use Cases

Converting simple chat instructions into production-ready system prompts
Refining legacy prompts that produce inconsistent or low-quality AI outputs
Hardening prompt security by adding strict negative constraints and guardrails