skills.rest
TheSethRose
TheSethRoseCOMMUNITY
Explore all skills by this author →
Website𝕏@TheSethRoseGitHub

prompt-engineering-patterns

Generate prompt patterns, templates, and evaluation steps for LLM production tasks.

Updated Feb 8, 2026
Search Tags:#templates#LLM#patterns#few-shot#prompt-engineering#system-prompts#CoT
One-click install
npx skills add https://github.com/TheSethRose/PeptideCalc --skill prompt-engineering-patterns-thesethrose
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-engineering-patterns
Source: https://github.com/TheSethRose/PeptideCalc/tree/main/.github/skills/prompt-engineering-patterns
Command: npx skills add https://github.com/TheSethRose/PeptideCalc --skill prompt-engineering-patterns-thesethrose

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Prompt engineering patterns provide a structured approach to designing, evaluating, and executing prompts that consistently produce reliable, high-quality outputs from LLMs, reducing guesswork and drift in production systems.

Core Features & Use Cases

  • Structured templates for prompts across generation, transformation, classification, and extraction.
  • Systematic evaluation patterns including A/B testing, CoT, and verification steps.
  • Deployment-ready workflows with versioning, monitoring, and guardrails.

Quick Start

Provide a production prompt task and the system will generate patterns, templates, and evaluation steps to deploy.

Frequently Asked Questions about prompt-engineering-patterns

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

FAQPage Schema
How do I write production-ready LLM prompts that don't drift in production?▼

Production-ready prompt engineering patterns unify structured templates, versioning, and evaluation workflows to maximize LLM reliability and controllability, systematically reducing output drift.

What is the best way to structure few-shot prompts and chain-of-thought for LLMs?▼

The best way to structure few-shot prompts and chain-of-thought (CoT) is applying standardized templates that guide the LLM through systematic reasoning, transformation, and verification steps.

How do I evaluate and A/B test system prompts at scale?▼

Evaluate and A/B test system prompts at scale by deploying structured evaluation workflows that incorporate verification steps, performance monitoring, and guardrails to ensure robust outputs.

Do I need numpy to implement prompt engineering patterns and structured outputs?▼

You need numpy installed as a dependency to run the scripts that generate and evaluate structured prompt engineering patterns, templates, and output workflows.

Can I use these prompt engineering templates for classification and extraction tasks?▼

Yes, you can use these prompt engineering templates for classification and extraction tasks, as they provide structured guidance specifically designed for generation, transformation, and data extraction.

Related Skills

Explore More Software Engineering Skills→
arbazkhan971arbazkhan971
26

prompt

Design, test, evaluate, and optimize prompts for LLM interactions.

Community
Advanced
caobingshengcaobingsheng
1

ai-product

Design, validate, test, and ship scalable prompt-driven features with safety checks.

Community
Advanced
archibatearchibate
6

prompt-engineering-patterns

Optimize LLM prompts with few-shot selection, reasoning patterns, and structured JSON outputs.

Community
Advanced
kinnerkarmanishkinnerkarmanish

meta-prompting

Optimize prompts through an iterative meta-prompt loop with evaluation metrics.

Community
Advanced