Claude Code Agent: Prompt Engineer — Design & Test Prompts

What it is

This is a Claude Code agent skill that specializes in prompt engineering. It helps developers design, optimize, and test LLM prompts systematically. Instead of manual trial-and-error prompt iteration, this agent applies structured techniques: chain-of-thought decomposition, few-shot example selection, output format specification, and A/B testing against evaluation criteria.

It targets AI application developers, prompt engineers, and teams building LLM-powered features who want to improve prompt quality without spending hours on manual iteration.

How it saves time or tokens

Manual prompt optimization is slow and subjective. This agent automates the iteration loop: write a prompt, test it against sample inputs, measure accuracy, suggest improvements, and re-test. It also identifies opportunities to reduce token usage by tightening instructions, removing redundant context, and restructuring prompts for efficiency.

How to use

1. Load the Prompt Engineer skill in your Claude Code environment.
2. Provide your current prompt, the target LLM, and sample inputs with expected outputs.
3. The agent analyzes the prompt, suggests improvements, and optionally runs benchmarks to compare versions.

Example

# Working with the Prompt Engineer agent:

User: Optimize this prompt for classification accuracy:
'Classify the following customer message as positive, negative, or neutral.'

Agent analysis:
- Missing: output format specification
- Missing: edge case handling (mixed sentiment)
- Suggestion: add few-shot examples
- Suggestion: specify JSON output format

Optimized prompt:
'Classify the customer message sentiment. Return JSON:
{"sentiment": "positive|negative|neutral", "confidence": 0.0-1.0}

Examples:
Input: "Great product, fast shipping" -> {"sentiment": "positive", "confidence": 0.95}
Input: "Item arrived damaged" -> {"sentiment": "negative", "confidence": 0.9}'

Related on TokRepo

• Prompt library — Browse reusable prompt templates and patterns
• AI coding tools — Tools for AI-assisted development

Common pitfalls

• Optimizing for one model does not guarantee improvement on another. Prompts tuned for Claude may behave differently on GPT-4 or Gemini. Test on your target model.
• Over-engineering prompts with too many constraints can reduce flexibility and increase token cost without meaningful accuracy gains.
• Benchmarks need representative test data. If your evaluation set is too small or biased, optimization may overfit to those specific examples.