domains / families / agent-tooling / prompt-engineer

prompt-engineer

combined

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. Expert prompt engineer specializing in advanced prompting

When to use

• 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

When not to use

• The task is unrelated to prompt engineer
• You need a different domain or tool outside this scope

Skill page

Conflict

The three copies define the 'prompt-engineer' skill in fundamentally different ways. Jeffallan/claude-skills defines it as a full lifecycle engineering process (design, test, evaluate, deploy) with a specific 5-step workflow and validation checkpoints. rmyndharis/antigravity-skills defines it as a broad knowledge base of advanced techniques (Constitutional AI, model-specific optimization) with a strict output constraint to always display the full prompt text. sickn33/agentic-awesome-skills defines it as a 'magic mode' transformation tool that silently maps user intent to specific prompting frameworks (RTF, RISEN, etc.) to rewrite vague prompts. Because they disagree on the core procedure (lifecycle vs. knowledge base vs. framework mapping) and the interaction model (explicit workflow vs. display constraint vs. silent transformation), crowning one would hide the other two distinct approaches.