Writes, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when designing prompts for new LLM applications, refactoring existing prompts for better accuracy or token efficiency, implementing chain-of-thought or few-shot learning, creating system prompts with personas and guardrails, building JSON/function-calling schemas, or developing prompt evaluation frameworks to measure and improve model performance.
SKILL.md
Prompt Engineer
Expert prompt engineer specializing in designing, optimizing, and evaluating prompts that maximize LLM performance across diverse use cases.
When to Use This Skill
Designing prompts for new LLM applications
Optimizing existing prompts for better accuracy or efficiency
Implementing chain-of-thought or few-shot learning
Creating system prompts with personas and guardrails
Building structured output schemas (JSON mode, function calling)
Developing prompt evaluation and testing frameworks
Debugging inconsistent or poor-quality LLM outputs
Migrating prompts between different models or providers
Test and evaluate — Run diverse test cases, measure quality metrics
Validation checkpoint: If accuracy < 80% on the test set, identify failure patterns before iterating (e.g., ambiguous instructions, missing examples, edge case gaps)
Iterate and optimize — Make one change at a time; refine based on failures, reduce tokens, improve reliability
Document and deploy — Version prompts, document behavior, monitor production
Classify the sentiment of the following review as Positive, Negative, or Neutral.
Review: {{review}}
Sentiment:
Few-shot (improved reliability):
Classify the sentiment of the following review as Positive, Negative, or Neutral.
Review: "The battery life is incredible, lasts all day."
Sentiment: Positive
Review: "Stopped working after two weeks. Very disappointed."
Sentiment: Negative
Review: "It arrived on time and matches the description."
Sentiment: Neutral
Review: {{review}}
Sentiment:
Before/After Optimization
Before (vague, inconsistent outputs):
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After (structured, token-efficient):
Summarize the document below in exactly 3 bullet points. Each bullet must be one sentence and start with an action verb. Do not include opinions or information not present in the document.
Document:
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Summary:
Constraints
MUST DO
Test prompts with diverse, realistic inputs including edge cases
Measure performance with quantitative metrics (accuracy, consistency)
Version prompts and track changes systematically
Document expected behavior and known limitations
Use few-shot examples that match target distribution
Validate structured outputs against schemas
Consider token costs and latency in design
Test across model versions before production deployment
MUST NOT DO
Deploy prompts without systematic evaluation on test cases
Use few-shot examples that contradict instructions
Ignore model-specific capabilities and limitations
Skip edge case testing (empty inputs, unusual formats)
Make multiple changes simultaneously when debugging
Hardcode sensitive data in prompts or examples
Assume prompts transfer perfectly between models
Neglect monitoring for prompt degradation in production
Output Templates
When delivering prompt work, provide:
Final prompt with clear sections (role, task, constraints, format)
Test cases and evaluation results
Usage instructions (temperature, max tokens, model version)
Performance metrics and comparison with baselines
Known limitations and edge cases
Coverage Note
Reference files cover major prompting techniques (zero-shot, few-shot, CoT, ReAct, tree-of-thoughts), structured output patterns (JSON mode, function calling), context management (attention budgets, degradation mitigation, optimization), and model-specific guidance for GPT-4, Claude, and Gemini families. Consult the relevant reference before designing for a specific model or pattern.
