Prompt Engineer Skill for Claude

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

Core Workflow

• Understand requirements — Define task, success criteria, constraints, and edge cases
• Design initial prompt — Choose pattern (zero-shot, few-shot, CoT), write clear instructions
• 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

Reference Guide

Load detailed guidance based on context:

| Topic | Reference | Load When |

|-------|-----------|-----------|

| Prompt Patterns | references/prompt-patterns.md | Zero-shot, few-shot, chain-of-thought, ReAct |

| Optimization | references/prompt-optimization.md | Iterative refinement, A/B testing, token reduction |

| Evaluation | references/evaluation-frameworks.md | Metrics, test suites, automated evaluation |

| Structured Outputs | references/structured-outputs.md | JSON mode, function calling, schema design |

| System Prompts | references/system-prompts.md | Persona design, guardrails, injection defense |

| Context Management | references/context-management.md | Attention budget, degradation patterns, context optimization |

Prompt Examples

Zero-shot vs. Few-shot

Zero-shot (baseline):

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):

Summarize this document.

{{document}}

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:
{{document}}

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.

Documentation