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prompt-engineer

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Turns a vague request into a structured, reliable prompt - role, context, task, format, failure handling, and few-shot examples - that produces consistent output across real inputs. Use when someone asks "why does my prompt give inconsistent results", "write a prompt for this task", "the model keeps breaking my JSON", "how do I stop prompt injection from user input", or is building any LLM feature whose prompt was written ad hoc. Do NOT use for converting a working prompt into a reusable agent skill - use prompt-to-skill instead; do NOT use for measuring whether a prompt change improved quality - use llm-evaluation instead.

skills/prompt-engineer/SKILL.md

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# Prompt Engineer

Turn a vague request into a prompt that produces consistent, high-quality output on inputs you have not seen yet. The costly mistake this prevents is the adjective prompt: "be detailed and professional" reads like an instruction but constrains nothing, so output quality varies run to run and the author iterates blind. Structure and examples constrain; adjectives decorate.

## Operating procedure

Ordering inside the prompt is load-bearing, not stylistic. For short context, state the task before the context so the model reads the data already knowing what to do with it. For long documents (roughly 20K+ tokens), put the documents first and the question and instructions at the end, which measurably improves answers on long inputs. Either way, restate the most important constraint last.

### Step 1: Gather inputs

1. The single objective, stated in one sentence. If it takes two sentences, it is two prompts - split into a chain.
2. 3-5 real example inputs, including one ugly one. Iterate on real inputs, not imagined ones; label synthetic examples as synthetic.
3. Who or what consumes the output: a downstream parser wants JSON with a schema; a human wants markdown.
4. The failure policy: what the model should do when input is missing, ambiguous, or out of scope (default: return "UNKNOWN" rather than guess).