How does Prompt Architect differ from prompt-forge?

Prompt Architect is a general-purpose framework for analyzing and refining task-specific prompts, while prompt-forge is specialized specifically for improving system prompts.

Can I use these prompts for models other than Claude?

Yes. While designed for the Claude Code environment, the structural principles and evidence-based techniques applied are effective across all major language models like GPT-4 and Gemini.

Does this skill require external MCP servers?

No, Prompt Architect operates entirely using Claude Code's built-in reasoning and file operation tools, requiring no external dependencies.

What is Phase 0 Expertise Loading?

Phase 0 is a pre-analysis step that checks for domain-specific knowledge in your codebase (like .yaml expertise files) to ensure the generated prompts align with existing patterns and conventions.

Prompt Architect

Name: Prompt Architect
Author: DNYoussef

byDNYoussef

•

Productivity & Workflow

Optimizes AI prompts through evidence-based engineering techniques, structural refinement, and systematic failure mode detection.

About

Prompt Architect is a sophisticated framework designed to transform casual AI queries into engineered artifacts through systematic analysis and refinement. It leverages research-backed techniques like Chain-of-Thought and Plan-and-Solve, while incorporating structural optimization to ensure high-quality, consistent outputs across various LLMs. Ideal for developers building programmatic prompts or complex workflows, it includes advanced features like domain-specific expertise loading and systematic failure mode detection to minimize errors and maximize response reliability.

Key Features

Systematic anti-pattern detection and failure mode analysis
6 GitHub stars
Phase 0 domain expertise loading for context-aware prompting
Structural optimization principles for improved attention and flow
Evidence-based patterns including Chain-of-Thought and Self-Consistency
Standardized output specification and quality verification steps

Use Cases

Designing robust prompt templates for production-grade AI applications
Standardizing prompt engineering workflows across development teams
Debugging and refining inconsistent or underperforming AI instructions

About

Key Features

Systematic anti-pattern detection and failure mode analysis
6 GitHub stars
Phase 0 domain expertise loading for context-aware prompting
Structural optimization principles for improved attention and flow
Evidence-based patterns including Chain-of-Thought and Self-Consistency
Standardized output specification and quality verification steps

Use Cases

Designing robust prompt templates for production-grade AI applications
Standardizing prompt engineering workflows across development teams
Debugging and refining inconsistent or underperforming AI instructions