What is the primary benefit of the Prompt Engineering Patterns skill?

It acts as a pre-processor for Claude, turning simple or vague user requests into detailed, structured instructions that the model can execute with much higher accuracy and fewer iterations.

Can this skill help with token management?

Yes, it includes specific patterns and best practices for removing redundancies, using hierarchical structures, and consistent abbreviations to maintain quality while reducing token consumption.

How does it handle vague requests like 'fix the code'?

It uses an expansion logic that identifies the task type, determines the likely files involved, sets success criteria, and breaks the fix down into root cause analysis, implementation, and verification steps.

Does it support structured data formats?

Absolutely. It provides specialized patterns for generating Pydantic-validated JSON, ensuring that AI responses can be reliably parsed by software systems without formatting errors.

Prompt Engineering Patterns

Name: Prompt Engineering Patterns
Author: eroslifestyle

byeroslifestyle

0•

생산성 및 워크플로

Optimizes and expands user requests using advanced prompt engineering techniques to maximize model performance, reliability, and controllability.

The Prompt Engineering Patterns skill is a sophisticated pre-processing utility designed to enhance the Claude Code experience by transforming vague or ambiguous instructions into high-fidelity technical requests. By applying industry-standard frameworks such as Chain-of-Thought (CoT), few-shot learning, and structured Pydantic outputs, it ensures that subsequent model actions are precise and efficient. It acts as a cognitive layer that analyzes request complexity, identifies specific task types (like bug fixes or refactoring), and applies specialized reasoning patterns to reduce errors and token waste in complex multi-agent orchestration workflows.

주요 기능

010 GitHub stars

02Token optimization strategies to reduce context window usage and costs

03Dynamic few-shot learning for consistent few-shot example selection

04Pydantic-based structured output enforcement for reliable JSON parsing

05Vague request expansion into detailed multi-step execution plans

06Structured reasoning patterns including Chain-of-Thought and Self-Verification

사용 사례

01Refining a 'fix' or 'optimize' request into a full architectural improvement plan

02Designing high-performance system prompts for specialized developer assistants

03Generating reliable JSON responses for integration with external APIs and tools

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add eroslifestyle/claude-orchestrator-plugin prompt-engineering-patterns

For use in Claude.ai and ChatGPT

주요 기능

010 GitHub stars

02Token optimization strategies to reduce context window usage and costs

03Dynamic few-shot learning for consistent few-shot example selection

04Pydantic-based structured output enforcement for reliable JSON parsing

05Vague request expansion into detailed multi-step execution plans

06Structured reasoning patterns including Chain-of-Thought and Self-Verification

사용 사례

01Refining a 'fix' or 'optimize' request into a full architectural improvement plan

02Designing high-performance system prompts for specialized developer assistants

03Generating reliable JSON responses for integration with external APIs and tools

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add eroslifestyle/claude-orchestrator-plugin prompt-engineering-patterns

For use in Claude.ai and ChatGPT