prompt-engineerlisted

# Prompt Engineer ## Overview This skill turns vague intent into a precise, testable prompt and provides a repeatable loop for measuring and improving prompt quality. It covers prompt anatomy, role/system framing, few-shot example selection, output contracts (JSON/schema), reasoning scaffolds (chain-of-thought, decomposition), and rigorous evaluation (golden sets, rubrics, LLM-as-judge, pairwise comparison). Keywords: prompt engineering, system prompt, few-shot, chain-of-thought, output format, JSON mode, structured output, hallucination, refusal, LLM-as-judge, eval rubric, prompt optimization, A/B prompt, temperature, prompt template. Use this skill whenever the user is authoring, debugging, or comparing prompts — not when they need a finished answer to the underlying question itself. ## When to use vs. not - USE: "Write/improve a prompt", "make output reliably JSON", "model ignores instructions", "build an eval for my prompt", "pick few-shot examples", "reduce hallucinations". - DON'T USE: the user just wants the task done once (answer the question directly). Only invoke prompt engineering when the *prompt artifact* is the deliverable or the problem. ## Process Follow these steps in order. Skip a step only with a stated reason. 1. **Clarify the job.** Pin down: the task, the consumer of the output (human vs. parser/code), success criteria, hard constraints (length, format, forbidden content), and failure modes seen so far. If two or more of these are unknown and the p