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prompt-engineer

Design prompts for chain-of-thought reasoning, few-shot learning, and structured output.

30|7|Updated Mar 1, 2026
Search Tags:#prompt engineering#optimization#chain-of-thought#ai#llm#few-shot learning
One-click install
npx skills add https://github.com/rfdiosuao/openfang-cn --skill prompt-engineer-rfdiosuao
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-engineer
Source: https://github.com/rfdiosuao/openfang-cn/tree/main/crates/openfang-skills/bundled/prompt-engineer
Command: npx skills add https://github.com/rfdiosuao/openfang-cn --skill prompt-engineer-rfdiosuao

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps users create more effective prompts for Large Language Models (LLMs), leading to more accurate, reliable, and cost-efficient AI outputs.

Core Features & Use Cases

  • Prompt Optimization: Designs prompts for chain-of-thought reasoning, few-shot learning, and structured output.
  • LLM Interaction: Provides expertise on controlling LLM behavior through parameters like temperature and top_p.
  • Use Case: A developer needs to generate consistent JSON outputs from an LLM. This Skill can craft a prompt that specifies a JSON schema, ensuring the LLM's response is machine-readable and predictable.

Quick Start

Use the prompt-engineer skill to generate a prompt for summarizing a long document into three bullet points.

Frequently Asked Questions about prompt-engineer

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

FAQPage Schema
How do I optimize LLM prompts for consistent JSON output?▼

To optimize LLM prompts for consistent JSON output, you design prompts that specify a JSON schema, ensuring the large language model's response is machine-readable and predictable.

What is chain-of-thought reasoning in prompt engineering?▼

Chain-of-thought reasoning in prompt engineering is a technique that guides large language models through sequential logical steps, generating more accurate and reliable AI outputs.

How do I use few-shot learning to control LLM behavior?▼

You use few-shot learning to control LLM behavior by providing examples within the prompt, which conditions the large language model to produce reproducible and structured outputs.

What is the best way to evaluate LLM performance for cost-efficient AI outputs?▼

The best way to evaluate LLM performance is by testing prompt variations against parameters like temperature and top_p, which helps generate reliable and cost-efficient AI outputs.

Why does my large language model output vary when using prompt optimization?▼

Large language model output varies during prompt optimization due to parameter settings like temperature and top_p, which control randomness and affect reproducibility.

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