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senior-prompt-engineer

Engineer prompts for large language models with structured outputs and evaluation.

1|Updated Mar 15, 2026
Search Tags:#prompt engineering#optimization#chain-of-thought#ai#llm#few-shot learning#evaluation
One-click install
npx skills add https://github.com/Pixel-Process-UG/superkit-agents --skill senior-prompt-engineer-pixel-process-ug
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: senior-prompt-engineer
Source: https://github.com/Pixel-Process-UG/superkit-agents/tree/main/templates/skills/senior-prompt-engineer
Command: npx skills add https://github.com/Pixel-Process-UG/superkit-agents --skill senior-prompt-engineer-pixel-process-ug

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the challenge of designing, optimizing, and managing prompts for large language models to achieve desired outputs consistently and effectively.

Core Features & Use Cases

  • Prompt Design: Systematically build prompts with clear identity, context, task, constraints, and output format.
  • Few-Shot Example Design: Create representative and diverse examples to guide the model.
  • Chain-of-Thought: Implement reasoning patterns for complex tasks.
  • Structured Output: Define precise output formats like JSON or Markdown.
  • Evaluation & Iteration: Develop datasets, define metrics, and A/B test prompts.
  • Prompt Versioning: Maintain a history of prompts with performance metrics.
  • Use Case: A developer needs to create a prompt that accurately classifies customer feedback into 'bug report', 'feature request', or 'general inquiry'. This skill guides them through defining the task, providing examples, specifying JSON output, and evaluating the prompt's accuracy.

Quick Start

Use the senior-prompt-engineer skill to design a prompt for classifying customer feedback into bug reports, feature requests, or general inquiries, ensuring the output is in JSON format.

Frequently Asked Questions about senior-prompt-engineer

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

FAQPage Schema
How do I design LLM prompts that consistently output structured JSON?▼

Design LLM prompts for structured JSON by defining explicit output specifications, establishing identity and constraints, and applying few-shot examples to guide the model into returning reliable structured outputs.

What is the best way to implement chain-of-thought patterns for complex AI tasks?▼

Implementing chain-of-thought patterns for complex AI tasks involves engineering prompts with systematic reasoning steps. This guides large language models through logical sequences, improving accuracy on complicated tasks before final output generation.

How do I evaluate and A/B test prompts using datasets and metrics?▼

Evaluate and A/B test prompts by developing specific datasets, defining quality metrics, and iterating on prompt versions. This structured evaluation process measures accuracy and performance, ensuring optimal prompt selection.

How do I use few-shot learning examples to guide a language model's classification task?▼

Use few-shot learning examples to guide classification tasks by creating representative and diverse examples within the prompt. This systematic design shows the language model the exact pattern to follow for classifying inputs accurately.

Can I maintain a history of prompt versions with their performance metrics?▼

Yes, you can maintain a history of prompt versions with performance metrics through prompt versioning. This facilitates tracking iteration results over time, allowing you to compare and manage prompt effectiveness systematically.

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