prompt-engineer

Optimizes prompts for LLMs to improve output accuracy and consistency.

Updated Mar 27, 2026

Search Tags:#chain-of-thought #llm #few-shot #prompt-engineering #system-prompt #prompt-optimization #prompt-testing

Ready to run

npx skills add https://github.com/CenredJun/openclaw-claudecode-setup-kit --skill prompt-engineer-cenredjun

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: prompt-engineer
Source: https://github.com/CenredJun/openclaw-claudecode-setup-kit/tree/main/skills/prompt-engineer
Command: npx skills add https://github.com/CenredJun/openclaw-claudecode-setup-kit --skill prompt-engineer-cenredjun

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The Prompt Engineer skill fixes inconsistent, ambiguous, or low-quality outputs from large language models by turning vague instructions into precise, testable prompts that produce repeatable, high-quality results.

Core Features & Use Cases

Prompt architecture: Design system and user prompts to establish persona, constraints, and expected output format for chatbots and agents.
Optimization techniques: Apply few-shot examples, chain-of-thought, constraint engineering, and self-consistency to improve accuracy and format compliance.
Debugging & testing: Run adversarial inputs, prompt ablation studies, and A/B comparisons to measure improvements and detect regressions.
Use Case: Improve a customer support assistant's refund-policy answers by reducing hallucinations and enforcing a strict JSON output schema.

Quick Start

Use prompt-engineer to analyze the current system prompt, propose three targeted edits to reduce ambiguity, and provide two validated example inputs with expected outputs.

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 to produce consistent and reliable outputs?▼

To optimize LLM prompts for consistent outputs, apply constraint engineering, few-shot examples, and chain-of-thought reasoning to reduce ambiguity. Iterative testing, adversarial input checks, and A/B comparisons further refine prompt accuracy and enforce strict format validation.

How can I design a system prompt to enforce a strict JSON output schema for chatbots?▼

Designing a system prompt to enforce strict JSON output requires defining explicit constraints, persona instructions, and expected output formats. Including validated few-shot examples and running prompt ablation studies helps detect regressions and maintain schema compliance.

What's the best way to test prompts against adversarial inputs and hallucinations?▼

The best way to test prompts against adversarial inputs and hallucinations is running adversarial input checks, prompt ablation studies, and A/B comparisons. These testing methods measure improvements, detect regressions, and validate format compliance for reliable LLM behavior.

Why does my large language model output vary when using multi-turn conversation flows?▼

Large language model output varies in multi-turn conversation flows due to ambiguous user prompt structures and lack of self-consistency techniques. Applying constraint engineering and few-shot examples stabilizes the context window and improves output repeatability across turns.

Can I use few-shot examples and chain-of-thought reasoning for prompt optimization?▼

Yes, you can use few-shot examples and chain-of-thought reasoning for prompt optimization to significantly improve accuracy and format compliance. These optimization techniques guide the model's logic and structure, reducing hallucinations and ensuring repeatable high-quality results.

When do I need prompt engineering for my LLM-powered features?▼

You need prompt engineering for LLM-powered features when outputs become inconsistent, ambiguous, or low-quality. It transforms vague instructions into precise, testable prompts, applying iterative testing and constraint engineering to achieve repeatable, high-quality results.