prompt-engineer-master

Create layered XML prompts with trust boundaries and numeric anchors.

7|1|Updated Apr 7, 2026

Search Tags:#prompt-engineering #ai-prompt #agent-workflows #librarian-agent #research-guide #xml-architecture

Ready to run

npx skills add https://github.com/Agent-Engineer-Master/skill-engineer --skill prompt-engineer-master

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: prompt-engineer-master
Source: https://github.com/Agent-Engineer-Master/skill-engineer/tree/main/operations/prompt-engineer-master
Command: npx skills add https://github.com/Agent-Engineer-Master/skill-engineer --skill prompt-engineer-master

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes assets (resource) and references (resource) components.

What problem does it solve?

This Skill helps create robust, repeatable prompt workflows (layered XML architecture) for AI agent prompts and multi-agent pipelines, enabling teams to design, validate, and debug complex prompts without rewriting each time.

Core Features & Use Cases

Produces production-grade prompt definitions using a layered XML structure with clear frontmatter (name, description) and a structured body.
Supports a Librarian sub-agent workflow for domain research, plus a rich references/repository of patterns and checklists to accelerate prompt design.
Enables orchestration of multi-agent prompts (orchestrator pattern) and RAG-like evidence handling with explicit trust boundaries.

Quick Start

State your task clearly and the skill will emit XML and prose prompt variants ready for use.

Frequently Asked Questions about prompt-engineer-master

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

FAQPage Schema

How do I structure production-grade AI prompts for multi-agent workflows?▼

Production AI prompts for multi-agent workflows use a layered XML architecture with clear frontmatter, structured bodies, and explicit trust boundaries to ensure robust orchestration. This approach enables repeatable design without constant rewriting.

What is the best way to prevent AI rationalization in complex prompt pipelines?▼

To prevent AI rationalization in complex prompt pipelines, apply anti-rationalization rules and numeric anchors within your prompt definitions. These production patterns constrain agent behavior and enforce strict evidence handling.

How do I design a system prompt for RAG-like evidence handling?▼

Designing a system prompt for RAG-like evidence handling requires setting explicit trust boundaries and using a structured XML body. This defines exactly how the agent processes retrieved context and orchestrates multi-source tasks.

Can I use a librarian sub-agent for domain research in my prompt workflow?▼

Yes, you can integrate a Librarian sub-agent workflow for domain research within your prompt orchestration. It runs alongside a references repository of patterns and checklists to accelerate and validate prompt design.

Does prompt engineering with XML architecture require external dependencies?▼

No, prompt engineering with XML architecture requires no external dependencies to generate prompt definitions. It operates standalone to emit XML and prose prompt variants directly from your stated task.

When should I use an orchestrator pattern for multi-agent prompts?▼

Use an orchestrator pattern for multi-agent prompts when coordinating complex pipelines that require robust validation and multi-source task execution. It establishes clear frontmatter and trust boundaries to manage agent interactions safely.