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name: prompt-engineer description: | Design, test, and optimize prompts for LLM interactions. Cover prompt patterns (few-shot, chain-of-thought, ReAct), system prompt design, output formatting, prompt evaluation, and prompt optimization techniques. Triggers on "write prompt", "optimize prompt", "design system prompt", "few-shot examples", "chain of thought", "prompt evaluation", "LLM output formatting", "prompt testing", or "prompt patterns".

Prompt Engineer

Overview

Design, test, and optimize prompts for LLM interactions. This skill covers prompt patterns (few-shot, chain-of-thought, ReAct), system prompt design, output formatting, prompt evaluation, and prompt optimization techniques.

Features

• Prompt patterns: few-shot, zero-shot, chain-of-thought, ReAct, self-consistency
• System prompt design: role definition, constraints, output format specification
• Output formatting: JSON, XML, markdown, structured templates
• Prompt evaluation: quality metrics, consistency testing, edge case analysis
• Prompt optimization: token reduction, clarity improvement, robustness testing

Usage

1. Identify the user's prompt need (pattern selection, system prompt, output format, or optimization)
2. Follow the corresponding workflow below
3. Produce structured outputs: prompt templates, system prompts, output schemas, or evaluation reports

Examples

• User: "Write a prompt for summarization" Agent: Runs Prompt Design workflow, selects zero-shot pattern, defines role and constraints, produces prompt with output format

• User: "Optimize this prompt" Agent: Runs Prompt Optimization workflow, identifies ambiguity, reduces token count, adds clarity, tests edge cases

• User: "Evaluate prompt quality" Agent: Runs Prompt Evaluation workflow, tests against quality metrics, identifies failure modes, produces improvement recommendations

When to Use

• Designing, versioning, and evaluating prompts for LLM-powered features
• Building agent workflows (ReAct, tool use, multi-agent coordination)
• Optimizing accuracy, format compliance, latency, and token cost
• Deploying guardrails, observability, and abuse defenses for GenAI in production

When NOT to Use

• Classical ML model training, feature engineering, or statistical A/B tests → use data-scientist
• General technical writing, API reference, or runbooks → use tech-writer-researcher
• Cloud infrastructure, CI/CD, or Kubernetes operations → use infrastructure-engineer
• Revenue recognition or finance close procedures → use senior-revenue-accountant
• Multi-feature token reduction roadmap → use ai-token-improvement-plan-engineer
• Rigorous token-efficiency experiments and ablations → use research-engineer-scientist-tokens

Core Workflows

1. Prompt Design Workflow

Step-by-step process:

1. Define the task clearly

   • What input does the user provide?
   • What output format is required?
   • What constraints must be enforced?

2. Choose the pattern

   Pattern	When	Structure
   Zero-shot	Simple, well-defined tasks	Instructions + input
   Few-shot	Pattern recognition, formatting	Examples + task
   Chain-of-thought	Reasoning, math, logic	"Let's think step by step"
   Role-based	Domain expertise needed	"You are a senior X..."
   Structured	API/programmatic consumption	JSON schema, XML template

3. Draft and iterate

   • Start simple, add complexity only where needed
   • Use clear separators (###, XML tags, markdown)
   • Specify output format explicitly
   • Include constraints and what to avoid

4. Test with edge cases

   • Empty input, malformed input, adversarial input
   • Boundary conditions
   • Multiple languages or formats

2. Prompt Optimization & Testing

Evaluation dimensions:

• Accuracy: Does it produce correct results? (human or model judge)
• Consistency: Same input → same output? (temperature, seed control)
• Format compliance: Does output match the schema? (JSON validator)
• Latency: Time to first token, total generation time
• Cost: Tokens consumed (input + output)

Testing workflow:

1. Build a benchmark dataset (50-200 diverse examples)
2. Establish baseline with current prompt
3. Modify one variable at a time (prompt, model, temperature)
4. Run A/B comparison on benchmark
5. Measure and document improvement

3. Agent Orchestration

Agent patterns:

Pattern	When	Components
ReAct	Tool-using agent	Reasoning + Action + Observation loop
Plan-and-Solve	Multi-step tasks	Planner → Executor → Checker
Reflexion	Self-improvement	Execute → Evaluate → Revise
Multi-agent	Complex workflows	Specialist agents + coordinator

Tool use checklist:

• Tool schemas are clearly defined (name, description, parameters)
• Agent can handle tool failure gracefully
• Tool results are summarized, not passed raw to user
• Rate limits and costs are monitored

4. Production Patterns

Security checklist:

• Input validated and sanitized
• Prompt injection defenses in place (delimiters, output filtering)
• No sensitive data in prompts (PII, secrets)
• Output filtered for harmful content
• Rate limiting and abuse detection

Observability:

• Log all prompts and responses (with PII redaction)
• Track token usage and cost per user/request
• Monitor for drift in output quality
• Alert on error rates and latency spikes