Will it make my prompts significantly longer?

No, one of the core principles is 'Refinement over Expansion.' It aims for precision and usually avoids making a prompt more than 3 times its original length unless necessary.

Which platforms does the Prompt Optimizer skill support?

It provides tailored optimization for all major platforms including Claude (XML), ChatGPT (Markdown), DeepSeek (CoT), Gemini, Doubao, and Zhipu GLM.

Does it handle missing information automatically?

It uses placeholders like [bracketed text] for missing details or asks you clarifying questions directly if specific context is required to make the prompt effective.

How does it improve my original prompt?

It uses a 4-D methodology to analyze intent, diagnose clarity issues, apply expert structures like role-playing or task decomposition, and deliver a concise, executable result.

Prompt Optimizer

Name: Prompt Optimizer
Author: TestAny-io

byTestAny-io

•

Productivity & Workflow

Transforms vague ideas into precise, high-performance AI prompts tailored for specific platforms like Claude, ChatGPT, and DeepSeek.

About

The Prompt Optimizer skill acts as a senior prompt engineer to refine and optimize AI instructions through a rigorous 4-D methodology: Deconstruct, Diagnose, Develop, and Deliver. It eliminates ambiguity, applies platform-specific syntaxes (such as XML for Claude or Chain-of-Thought for DeepSeek), and ensures prompts remain concise and actionable without unnecessary filler. By iteratively polishing inputs and enforcing negative constraints, it helps users unlock the full potential of large language models for creative, technical, or complex reasoning tasks.

Key Features

Strict Negative Constraints to prevent prompt bloat and fluff
4-D Optimization Methodology (Deconstruct, Diagnose, Develop, Deliver)
Iterative Self-Evaluation against professional quality standards
6 GitHub stars
Smart Placeholder Management for missing contextual information
Multi-Platform Syntax Support (Claude XML, ChatGPT Markdown, DeepSeek CoT)

Use Cases

Migrating and adapting prompts between different LLM providers (e.g., GPT to Claude)
Converting rough requirements into structured system prompts for AI agents
Refining complex technical instructions to reduce ambiguity and improve output accuracy

About

Key Features

Strict Negative Constraints to prevent prompt bloat and fluff
4-D Optimization Methodology (Deconstruct, Diagnose, Develop, Deliver)
Iterative Self-Evaluation against professional quality standards
6 GitHub stars
Smart Placeholder Management for missing contextual information
Multi-Platform Syntax Support (Claude XML, ChatGPT Markdown, DeepSeek CoT)

Use Cases

Migrating and adapting prompts between different LLM providers (e.g., GPT to Claude)
Converting rough requirements into structured system prompts for AI agents
Refining complex technical instructions to reduce ambiguity and improve output accuracy