prompt-optimizer

Analyze user prompts to identify intent and missing requirements.

1|Updated Apr 6, 2026

Search Tags:#workflow #optimization #prompt #context-management #ai-guidance

Ready to run

npx skills add https://github.com/vrcms/everything-qwen-code --skill prompt-optimizer-vrcms

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: prompt-optimizer
Source: https://github.com/vrcms/everything-qwen-code/tree/main/.qwen/skills/prompt-optimizer
Command: npx skills add https://github.com/vrcms/everything-qwen-code --skill prompt-optimizer-vrcms

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill addresses the challenge of vague or ineffective user prompts by analyzing intent, identifying missing context, and mapping requests to the most effective ECC components.

Core Features & Use Cases

Intent Analysis: Automatically classifies user requests into categories like feature development, bug fixing, or refactoring.
Component Matching: Recommends the best-fit agents, skills, and commands based on the detected tech stack and project scope.
Use Case: If you are unsure how to structure a complex request for a new feature, this skill will critique your draft and provide a professional, ready-to-execute prompt that includes necessary verification steps and architectural planning.

Quick Start

Use the prompt-optimizer skill to rewrite my request for building a new user authentication module.

Frequently Asked Questions about prompt-optimizer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I optimize prompts for complex development workflows?▼

To optimize prompts for complex development workflows, the skill analyzes raw requests to identify intent, missing requirements, and project context. It matches tasks to specific agents and commands, generating structured templates that ensure high-quality execution.

What is the best way to structure an AI prompt for feature development?▼

Structuring an AI prompt for feature development involves classifying the request, mapping it to the correct tech stack, and including necessary verification steps. This ensures your prompt contains required architectural planning and project context.

Can I use prompt optimization to fix vague AI requests for bug fixing?▼

Yes, you can use prompt optimization to fix vague AI requests for bug fixing. It automatically classifies user requests and identifies missing context, providing professional ready-to-execute prompts that address the actual issue.

Does the prompt optimizer work with any tech stack?▼

Yes, the prompt optimizer works with any tech stack. It analyzes your raw input to detect the project scope and recommends the best-fit agents, skills, and commands based on the specific technologies involved.

Why does my AI prompt fail to execute complex refactoring tasks?▼

AI prompts often fail complex refactoring tasks because they lack intent classification and architectural planning. Identifying missing requirements and mapping requests to specialized agents provides structured templates that prevent execution failure.

its-Basudeba

prompt-optimizer

Analyzes raw prompts and outputs structured, ready-to-use versions incorporating ECC ecosystem components and best practices for planning, testing, and verification.

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