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iinoshirozheng
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prompt-engineering-patterns

Organize reusable prompt patterns and templates for structured LLM outputs.

Updated Aug 23, 2026
Search Tags:#templates#patterns#few-shot#structured-output#prompt#cot
One-click install
npx skills add https://github.com/iinoshirozheng/SinoCloudChat --skill prompt-engineering-patterns-iinoshirozheng
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-engineering-patterns
Source: https://github.com/iinoshirozheng/SinoCloudChat/tree/main/.agents/skills/prompt-engineering-patterns
Command: npx skills add https://github.com/iinoshirozheng/SinoCloudChat --skill prompt-engineering-patterns-iinoshirozheng

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

Prompts for LLMs often drift, produce inconsistent results, and require extensive trial-and-error. This Skill provides a library of proven prompt patterns and templates to standardize how tasks are framed, how responses are structured, and how results are evaluated, enabling faster, more reliable development at scale.

Core Features & Use Cases

  • Pattern Library: a collection of few-shot, chain-of-thought, structured-output, and system-prompt patterns that can be composed into new prompts.
  • Template Systems: modular components to assemble prompts without rewriting logic.
  • Evaluation & Optimization: guidance on A/B testing, metrics, and versioning for prompts.
  • Use Cases: production prompts for reasoning, data extraction, classification, and generation tasks.

Quick Start

Try a core pattern from the library to build a reliable, structured prompt for a given task.

Frequently Asked Questions about prompt-engineering-patterns

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

FAQPage Schema
How do I standardize LLM prompts to stop inconsistent outputs in production?▼

Prevent prompt drift by using versioned templates and testing hooks to evaluate and standardize prompt behavior. This approach enforces structured outputs and safety checks to improve reliability across production environments.

What is the best way to build chain-of-thought reasoning and few-shot prompts?▼

Build chain-of-thought and few-shot prompts by composing modular patterns from a dedicated library. This allows you to assemble complex reasoning structures without rewriting underlying logic, ensuring reliable task framing.

How do I enforce structured outputs for LLM data extraction and classification tasks?▼

Enforce structured outputs for data extraction by applying structured-output patterns and system prompts. These templates constrain the model response format, reducing parsing errors and improving data reliability.

Can I use these prompt templates for A/B testing and prompt versioning?▼

Yes, you can use these templates for A/B testing and prompt versioning. The system includes evaluation and optimization guidance with metrics and testing hooks to measure and compare prompt performance reliably.

Does this prompt engineering library require specific dependencies or environments?▼

This prompt engineering library requires the numpy dependency to function. It provides scripts, references, and assets designed to integrate into advanced software engineering workflows for production-grade LLM applications.

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