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…
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What the file says
# 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
1. **Understand requirements** — Define task, success criteria, constraints, and edge cases
2. **Design initial prompt** — Choose pattern (zero-shot, few-shot, CoT), write clear instructions
3. **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)
…
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GET https://markdownregistry.com/api/v1/artifacts/art_by3yob26nqqvobdw
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GET https://markdownregistry.com/api/v1/blob/1ab0f43e1c6d91501ffe2d432914b9f2b4462e071f4127c54536c186e36c4b81
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