Why use XML tags instead of standard Markdown?

While Markdown is excellent for human readability, XML tags act as explicit attention anchors for LLMs, making it significantly easier for the model to isolate and reference specific sections of data in high-token environments.

What is 'tag soup' and why does this skill avoid it?

Tag soup occurs when XML tags are nested too deeply or used for every single paragraph. This skill enforces a flat-first structure to ensure the hierarchy adds clarity rather than unnecessary complexity that can confuse the model.

Does this skill increase my token usage?

XML tagging adds a minimal amount of tokens for the tags themselves, but it drastically reduces 'context rot,' meaning the model is more likely to follow instructions correctly the first time, saving you tokens on revisions.

Can I use this for conversational prompts?

This skill is best suited for high-density environments like system prompts or complex technical instructions. For casual conversation, light Markdown is usually sufficient.

XML Context Architect

Name: XML Context Architect
Author: mahidalhan

bymahidalhan

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Productivity & Workflow

Transforms unstructured instructions into semantic XML structures to optimize LLM attention and retrieval accuracy.

About

XML Context Architect is a specialized skill for Claude Code designed to solve the problem of 'context rot' in large prompts. By applying principles of attention economics, it structures raw data, instructions, and background information into semantically meaningful XML tags. This approach creates clear cognitive boundaries and retrieval anchors, ensuring that Claude remains focused on high-priority signals while navigating complex, multi-part tasks. It is an essential tool for prompt engineers and developers building sophisticated AI agents that require precise instruction following.

Key Features

Flat-first hierarchical structuring to minimize token depth
Retrieval-optimized naming conventions for reliable model reference
18 GitHub stars
Semantic XML tagging for optimized cognitive boundaries
Attention-aware density management to filter low-signal noise
Canonical section patterning for standardized prompt architecture

Use Cases

Structuring complex system prompts for autonomous AI agents
Organizing massive reference datasets within a context window for better RAG performance
Defining strict multi-layered constraints and edge-case examples for code generation

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add mahidalhan/skilled-intelligence-marketplace xml-context-engineering

For use in Claude.ai and ChatGPT

Download Skill

GitHub