prompt-engineer_skill

Overview

This skill helps design, optimize, and test prompts for AI models using systematic prompt engineering techniques. It applies few-shot learning, chain-of-thought reasoning, role-based framing, and structured output templates to produce reliable, reproducible prompts. The goal is higher model accuracy, clearer outputs, and faster iteration for production use.

How this skill works

The skill generates tailored system and user prompt templates, injects examples for few-shot learning, and adds explicit output schemas for structured responses. It can introduce chain-of-thought scaffolding when stepwise reasoning is needed and crafts role-based instructions to bias model behavior toward a target persona or function. It also recommends test cases and evaluation prompts to validate and refine prompt performance.

When to use it

• Creating initial prompts for a new AI-powered feature or agent
• Improving accuracy or consistency of model outputs across inputs
• Designing structured JSON or table outputs for downstream processing
• Guiding models to follow a specific persona or review checklist
• Diagnosing why a model produces hallucinations or format drift

Best practices

• Start with a clear system role and concise core responsibilities to reduce ambiguity
• Use 2–5 high-quality examples for few-shot learning rather than many noisy ones
• Explicitly specify output format and validation rules to prevent format errors
• Prefer short, deterministic instructions for extraction tasks; enable chain-of-thought only when transparency is needed
• Iteratively test with edge cases and measure performance with concrete metrics (accuracy, parse rate)

Example use cases

• Design a prompt that extracts contact information as validated JSON from free text
• Create a code-review persona prompt that prioritizes security and performance findings
• Optimize a customer-support triage prompt to classify tickets into priority buckets
• Build a stepwise math-reasoning prompt that explains each calculation before answering
• Convert vague business requirements into a structured checklist for an LLM-driven workflow