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prompt-ingenieur

Design structured prompts with roles, context, constraints, and verification templates.

Updated Aug 23, 2026
Search Tags:#workflow#templates#llm#guardrails#prompt-engineering#anti-hallucination#self-verification
One-click install
npx skills add https://github.com/rcliment1987/skills-bimsmarter --skill prompt-ingenieur
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-ingenieur
Source: https://github.com/rcliment1987/skills-bimsmarter/tree/main/prompt-ingenieur
Command: npx skills add https://github.com/rcliment1987/skills-bimsmarter --skill prompt-ingenieur

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The Prompt Engineer skill provides a structured, repeatable approach to crafting prompts that reliably produce high-quality outputs from any LLM. It helps teams reduce drift, improve consistency across models, and scale prompt design with templated patterns, guardrails, and verification steps.

Core Features & Use Cases

  • Structured prompt templates (Role, Context, Task, Constraints, Output, Examples) that enforce consistency
  • Anti-hallucination patterns, safety guardrails, and self-verification to improve reliability
  • Template-driven generation for system prompts, agent prompts, and multi-step workflows
  • Built-in quality assurance, edge-case handling, and auditing capabilities
  • Rapid prototyping and cross-model testing to validate prompts at scale
  • Use cases include production prompts for chatbots, agents, and automated workflows

Quick Start

Replace the domain placeholders in the templates with your target task, then adapt roles, context, and constraints to your workflow to start creating robust prompts.

Frequently Asked Questions about prompt-ingenieur

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

FAQPage Schema
How do I create production-grade prompts that yield top 5% outputs from any LLM?▼

Reduce LLM hallucination by implementing anti-hallucination patterns, safety guardrails, and self-verification steps within structured prompt architectures to improve output reliability and consistency.

How do I build multi-step prompt architectures for automated workflows?▼

Scale prompt design across teams by applying template-driven generation patterns with structured frontmatter and quality assurance templates, enabling rapid prototyping and consistent cross-model testing.

What is the best way to structure system prompts for chatbots and agents?▼

Structure system prompts by enforcing consistent blocks for role, context, task, constraints, output, and examples, which enables repeatable, auditable prompt design for chatbots and agents.

Why does my LLM output drift between different models and how can I fix it?▼

Fix LLM output drift between models by codifying production-grade prompts with structured constraints, context gathering, and quality assurance templates that enforce consistency across different LLMs.

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