prompt-engineeringlisted

# Prompt Engineering Design prompts and evals as a first-class engineering deliverable. `context-engineering` assembles context for a coding agent; this skill designs the prompt-as-product surface — the prompts, model choices, guardrails, and eval harnesses behind LLM-powered features. A prompt without an eval is an opinion; an eval without a prompt is a benchmark. Ship neither blind. ## When to use - Designing an LLM-powered feature (chat, summarization, extraction, classification, generation) - Building or refining a prompt for production use - Creating an eval harness to measure prompt/model quality - Selecting a model for a specific task against cost/latency/quality trade-offs - Triggers on "prompt engineering", "LLM feature", "eval harness", "prompt design", "提示词工程", "LLM 特性" **Not for:** assembling context for a coding agent (use `context-engineering`); general research on a topic (use `research`); API contract design for non-LLM endpoints (use `api-design`). ## Steps ### 1. Define the task and success criteria State the task in one sentence, then define measurable success criteria — without these, prompt iteration is vibes-driven. Pull from the product spec: - Input space: what inputs will the prompt receive? (vary by length, language, edge case, adversarial) - Output contract: structured output (JSON schema), free text, or classification? - Quality bar: accuracy %, format adherence %, hallucination rate, latency target, cost per call - Failure modes to prevent