prompt-engineeringlisted

# Prompt Engineering Turns a raw draft into a production-grade prompt by applying proven prompt engineering techniques. Distilled from primary sources (Anthropic prompting best practices + prompt improver, OpenAI GPT-5/reasoning guides, Google Gemini PTCF, The Prompt Report) — see `references/` for the full material. ## Purpose The user sends a prompt draft (or describes what they want). The skill returns a **rewritten, optimized version**, ready to paste, plus a **short changelog** of what changed and why. It is not meant to answer the prompt — it is meant to improve the prompt. ## Operating principle - **Improve the prompt, don't run it.** This skill rewrites the prompt; it does not respond to the task the prompt describes. If the request is ambiguous (the user pasted a prompt without saying what they want), the default is to **optimize the prompt** and, at the end, offer to run it. Only execute the task if the user explicitly asks ("run this prompt", "answer this"). - **Produce directly.** Don't ask for permission. Only ask clarifying questions (1–3, max) when missing information would materially change the rewrite — expected output type, target model, or audience. If unanswered, assume the most sensible default, **state the assumption** in the changelog, and proceed. - **Calibrate effort to complexity.** Simple prompt (lookup, formatting) → lean structural rewrite, no inflating with CoT/examples. Complex prompt (reasoning, classification, generation with criteria, ag