Prompt Engineering

This is your go-to when you're writing prompts for other LLMs, whether that's crafting system prompts, building few-shot examples, or optimizing production templates. It covers the essential patterns like chain-of-thought reasoning, progressive disclosure, and template systems with actual examples you can adapt. The practical stuff is solid: it shows you how to iterate from simple to complex prompts, integrate with RAG systems, and avoid common mistakes like over-engineering before testing basics. Worth using whenever you're designing commands for agents or sub-agents, especially if you need consistent outputs at scale and want to squeeze better performance without burning tokens.

skills.sh installs: 1.5K
GitHub stars: 1.8K
last updated: Aug 21, 2026

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Install

$ npx -y skills add neolabhq/context-engineering-kit --skill prompt-engineering --agent claude-code

Installs into .claude/skills of the current project.

Files

Prompt Engineering Patterns

Advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability.

Core Capabilities

1. Few-Shot Learning

Teach the model by showing examples instead of explaining rules. Include 2-5 input-output pairs that demonstrate the desired behavior. Use when you need consistent formatting, specific reasoning patterns, or handling of edge cases. More examples improve accuracy but consume tokens—balance based on task complexity.

Example:

Extract key information from support tickets:

Input: "My login doesn't work and I keep getting error 403"
Output: {"issue": "authentication", "error_code": "403", "priority": "high"}

Input: "Feature request: add dark mode to settings"
Output: {"issue": "feature_request", "error_code": null, "priority": "low"}

Now process: "Can't upload files larger than 10MB, getting timeout"