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Mohamed-Elkahef
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prompt-engineering

Standardize prompt design patterns for LLM-powered agent outputs.

Updated Mar 5, 2026
Search Tags:#prompt engineering#chain-of-thought#few-shot learning#agent design#system prompts#tool usage#LLM agents
One-click install
npx skills add https://github.com/Mohamed-Elkahef/test-project --skill prompt-engineering-mohamed-elkahef
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-engineering
Source: https://github.com/Mohamed-Elkahef/test-project/tree/main/.claude/skills/prompt-engineering
Command: npx skills add https://github.com/Mohamed-Elkahef/test-project --skill prompt-engineering-mohamed-elkahef

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill eliminates the inconsistent, unreliable outputs from LLMs when building agentic systems, removing the need for time-consuming trial-and-error prompt tuning.

Core Features & Use Cases

  • Structured Prompt Design: Create system prompts, few-shot examples, and chain-of-thought templates tailored for LLM agent tasks.
  • Tool Usage & Constraint Guidance: Define clear tool call patterns and operational constraints to reduce agent hallucinations and unexpected behavior.
  • Use Case: For example, use this Skill to build a SQL generation agent that always retrieves table schemas before querying, cutting hallucinated table errors by over 90%.

Quick Start

Use the prompt-engineering skill to design a system prompt for a customer support agent that returns order status updates in structured JSON format.

Frequently Asked Questions about prompt-engineering

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

FAQPage Schema
How do I standardize LLM system prompts for consistent agent outputs?▼

Standardize LLM system prompts by applying structured design patterns for tool usage constraints, chain-of-thought reasoning, and few-shot example integration to eliminate inconsistent, unreliable outputs from agentic systems.

What is the best way to reduce hallucinations in LLM agents using prompt engineering?▼

Reduce LLM agent hallucinations by defining clear tool call patterns and operational constraints within your prompt design, ensuring agents follow strict workflows like retrieving schemas before querying to cut errors by over 90%.

Can I use few-shot learning and chain-of-thought templates for SQL generation agents?▼

Yes, you can use few-shot learning and chain-of-thought templates to build SQL generation agents that reliably retrieve table schemas before querying, significantly reducing hallucinated table errors in data workflows.

How do I format structured JSON outputs from a customer support LLM agent?▼

Format structured JSON outputs from a customer support LLM agent by designing a system prompt that enforces strict output formatting constraints, ensuring order status updates are consistently returned in the required structure.

Do I need prompt versioning for production LLM agent deployments?▼

Yes, prompt versioning is required for production LLM agent deployments to maintain consistent, high-quality outputs across system prompts and few-shot examples as agentic systems scale across software engineering workflows.

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