prompt-improving

Name: prompt-improving
Availability: InStock
Author: xtrm-dev

Transform raw prompts into structured XML prompts with semantic tags and reasoning scaffolds.

4|1|Updated Nov 1, 2025

Search Tags:#prompt engineering #clarification #chain of thought #task structuring #xml formatting #multishot examples

Ready to run

npx skills add https://github.com/xtrm-dev/specialists --skill prompt-improving

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: prompt-improving
Source: https://github.com/xtrm-dev/specialists/tree/main/.xtrm/skills/default/prompt-improving
Command: npx skills add https://github.com/xtrm-dev/specialists --skill prompt-improving

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill removes ambiguity from raw prompts and reshapes them into clearer, better-structured requests that are easier for the model to execute effectively.

Core Features & Use Cases

Prompt Structuring: Wraps requests in semantic XML so intent, parameters, and instructions are easier to understand.
Context-Specific Enhancement: Adds thinking scaffolds for analysis, examples for development tasks, and constraints for refactoring tasks.
Ambiguity Handling: Detects vague prompts and asks for clarification when the request is too underspecified to improve safely.
Use Case: A user types a short prompt like “analyze logs” and the skill rewrites it into a detailed, task-ready prompt with structure and expectations.

Quick Start

Use the prompt-improving skill to rewrite your raw prompt into a clearer XML-structured request and then execute the improved version.

Frequently Asked Questions about prompt-improving

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

FAQPage Schema

How do I turn a vague prompt into a clear request?▼

To turn a vague prompt into a clear request, semantic XML formatting is applied to wrap requests, separating intent, parameters, and instructions so the model can execute them effectively.

What is the best way to structure prompts for complex analysis tasks?▼

The best way to structure prompts for complex analysis tasks is adding chain-of-thought reasoning scaffolds and multishot examples. This provides step-by-step thinking guidance and context-specific constraints.

How do I fix ambiguity in prompt engineering?▼

To fix ambiguity in prompt engineering, the system detects vague inputs and asks for clarification when the request is too underspecified to improve safely, ensuring the final structured prompt is accurate.

When do I need XML formatting for prompt structuring?▼

You need XML formatting for prompt structuring when your requests benefit from clear scope separation, such as refactoring or development tasks requiring explicit constraints, examples, and reasoning scaffolds.

Does this approach work for development and refactoring tasks?▼

Yes, this approach works for development and refactoring tasks by adding context-specific enhancements like multishot examples for development and explicit constraints for refactoring to improve execution quality.