What is the primary benefit of using the architecting-prompts skill?

It provides a standardized, professional framework for creating prompts that maximize AI reasoning capabilities while minimizing token waste and 'hallucination' risks through structured attention management.

How does this skill help with sycophancy in AI models?

The skill enforces a 'Truth-First' protocol, training the AI to prioritize technical accuracy and contradict flawed user suggestions rather than providing agreeable but incorrect responses.

Is this skill used for generating prompt files?

No, this skill is for the design, optimization, and auditing of prompt logic. For actually generating .md prompt files for pipelines, the 'generating-prompts' skill is recommended.

When should I use XML tags instead of standard Markdown?

Use XML tags only when you have data isolation needs (over 50 lines of raw data), strict 'MUST/NEVER' constraints, or require a clear boundary for internal reasoning (thought blocks).

AI Prompt Architect

Name: AI Prompt Architect
Author: Git-Fg

byGit-Fg

•

Data Science & ML

Architects high-performance AI prompts using advanced complexity-based standards, attention management, and structural optimization patterns.

About

The Architecting Prompts skill provides a rigorous framework for designing, optimizing, and auditing complex AI instructions and multi-stage chains. Based on 2026 Complexity-Based Guidance, it empowers developers to manage model attention through a strategic XML/Markdown decision matrix, prevents sycophancy to ensure objective outputs, and implements advanced design patterns like Chain-of-Thought and Few-Shot learning. It is specifically built for engineering production-grade AI interactions where signal-to-noise ratios and strict constraint enforcement are critical for success.

Key Features

Advanced design patterns including CoT, Few-Shot, and Constraint Encoding
Multi-stage quality gates for verifying production-ready system instructions
1 GitHub stars
2026 Complexity-Based Guidance for high-fidelity prompt engineering
Strategic XML/Markdown decision matrix for optimized signal-to-noise ratios
Built-in Sycophancy Prevention to ensure objective and truthful AI responses

Use Cases

Implementing strict data isolation and constraint enforcement in RAG pipelines
Designing robust system prompts for complex multi-agent workflows
Auditing and optimizing legacy prompts to reduce token noise and improve reasoning

About

Key Features

Advanced design patterns including CoT, Few-Shot, and Constraint Encoding
Multi-stage quality gates for verifying production-ready system instructions
1 GitHub stars
2026 Complexity-Based Guidance for high-fidelity prompt engineering
Strategic XML/Markdown decision matrix for optimized signal-to-noise ratios
Built-in Sycophancy Prevention to ensure objective and truthful AI responses

Use Cases

Implementing strict data isolation and constraint enforcement in RAG pipelines
Designing robust system prompts for complex multi-agent workflows
Auditing and optimizing legacy prompts to reduce token noise and improve reasoning