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prompt-engineering

Guide LLM prompt authoring with clarity, context, and formatting techniques.

Updated Jan 7, 2026
Search Tags:#prompt engineering#ai#llm#agent#instructions#language models
One-click install
npx skills add https://github.com/lbussell/agent-skills --skill prompt-engineering-lbussell
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-engineering
Source: https://github.com/lbussell/agent-skills/tree/main/skills/prompt-engineering
Command: npx skills add https://github.com/lbussell/agent-skills --skill prompt-engineering-lbussell

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps users write more effective prompts for Large Language Models (LLMs) by providing principles, techniques, and examples for clear, concise, and actionable instructions.

Core Features & Use Cases

  • Instruction Clarity: Learn to be explicit and provide context for better LLM understanding.
  • Formatting Control: Utilize XML tags and style matching to guide output structure.
  • Tool Usage Calibration: Optimize how LLMs interact with tools, from defaulting to action to sequential calls.
  • Long-Running Tasks: Implement strategies for context management, state tracking, and resuming complex tasks.
  • Agentic Coding Practices: Encourage code exploration, minimize hallucinations, and avoid over-engineering.
  • Frontend Design Aesthetics: Guide LLMs to create distinctive and engaging user interfaces.

Quick Start

Use the prompt-engineering skill to learn how to make LLM instructions more explicit.

Frequently Asked Questions about prompt-engineering

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

FAQPage Schema
How do I write effective prompts for large language models?▼

Effective prompts for large language models require explicit instructions, sufficient context, and clear examples to improve LLM output quality, reliability, and adherence to user intent.

What is the best way to format LLM instructions for complex tasks?▼

The best way to format LLM instructions is using XML tags and style matching to control output structure, alongside strategies for context management and state tracking during long-running tasks.

How can I stop an AI agent from hallucinating during code generation?▼

To stop an AI agent from hallucinating during code generation, apply agentic coding practices that encourage code exploration, minimize hallucinations, and actively avoid over-engineering the solution.

How do I manage context when an LLM is running long-running tasks?▼

Manage context during long-running LLM tasks by implementing strategies for context management, state tracking, and resuming complex tasks to maintain output reliability over time.

Can I guide language models to create better frontend design aesthetics?▼

You can guide language models to create better frontend design aesthetics by crafting prompts that direct the LLM to generate distinctive and engaging user interfaces.

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