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prompt-engineer

Designs and evaluates system prompts, few-shot examples, and chain-of-thought structures for LLM applications.

2|Updated Jan 10, 2026
Search Tags:#prompt engineering#llm#system prompt#chain of thought#prompt injection#few-shot#prompt evaluation
One-click install
npx skills add https://github.com/Shubh2310-developer/ENGUNITYCORE --skill prompt-engineer-shubh2310-developer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-engineer
Source: https://github.com/Shubh2310-developer/ENGUNITYCORE/tree/main/.claude/skills/prompt-engineer
Command: npx skills add https://github.com/Shubh2310-developer/ENGUNITYCORE --skill prompt-engineer-shubh2310-developer

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Writing prompts that LLMs reliably follow is difficult: vague instructions, missing output format specifications, and untested changes lead to inconsistent model behavior. This Skill applies engineering rigor to prompt design so instructions produce predictable, measurable results. ## Core Features & Use Cases - Structured System Prompt Design: Organizes prompts into role, context, instructions, constraints, output format, and examples sections. - Few-Shot and Chain-of-Thought Patterns: Provides guidance for building diverse example sets and eliciting step-by-step reasoning from models. - Prompt Evaluation and Anti-Pattern Detection: Identifies common failures such as vague instructions, kitchen-sink prompts, and prompt injection vulnerabilities, with severity-rated fixes. - Use Case: When building a customer support chatbot, use this Skill to architect a system prompt with explicit constraints, format specifications, and defensive instructions against injection attacks. ## Quick Start Ask the prompt-engineer skill to design a structured system prompt with output format constraints and few-shot examples for your LLM feature.

Frequently Asked Questions about prompt-engineer

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

FAQPage Schema
How do I write an effective system prompt for an LLM?▼

Structure the system prompt into clear sections: role, context, instructions, constraints, output format, and examples. Be explicit about what the model should not do, and specify the expected output structure rather than assuming the model will infer it.

How many few-shot examples should a prompt include?▼

Include 2-5 diverse examples that match the difficulty of expected inputs. Cover edge cases, keep formatting consistent across examples, and add negative examples when they clarify boundaries.

When should I use chain-of-thought prompting?▼

Use chain-of-thought prompting when tasks require multi-step reasoning or when debugging model failures. Ask the model to think step by step, provide a reasoning structure, and parse the reasoning separately from the final answer.

How do I defend prompts against prompt injection attacks?▼

Treat user input as untrusted and include explicit instructions about what the model must not do, such as ignoring embedded commands. Combine clear constraint sections with input validation and test the prompt against adversarial inputs.

Why does my LLM output ignore the requested format?▼

Models default to free-form text when the output format is not specified explicitly. State the exact structure expected, show a formatted example, and avoid imprecise language that leaves the format open to interpretation.

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