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Prompt engineering

Skill minagayid/Agents/skills/library/prompt-engineering

Design, refine, and debug LLM prompts for reliable, structured outputs. Use when the user asks to write a prompt, improve prompt reliability, reduce hallucination, or get structured output.From its SKILL.md

Install
npx -y skills add minagayid/Agents --skill prompt-engineering

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SKILL.md

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Prompt Engineering

Craft prompts that get reliable, well-shaped results from an LLM.

Structure a prompt

  1. Role / context — who the model is and the task's purpose.
  2. Task — a single, specific instruction. Split unrelated tasks.
  3. Inputs — clearly delimited (XML tags, fenced blocks) and separated from instructions.
  4. Constraints — length, tone, what to avoid, how to handle unknowns ("say 'unknown' if not stated").
  5. Output format — exact schema; ask for JSON/keys explicitly when parsing downstream.
  6. Examples — 1–3 few-shot examples covering the tricky/edge cases (huge reliability lever).

Techniques

  • Be specific. Vague prompts get vague answers; state exactly what "good" looks like.
  • Let it think. For reasoning tasks, allow step-by-step working before the final answer.
  • Ground it. Provide the source material and instruct "answer only from the context above" to cut hallucination; ask for citations/quotes.
  • Structured output. Give a schema and one example; consider tool/function calling or a constrained-decoding library (e.g. Instructor) for guaranteed shapes.
  • Set an escape hatch. Tell the model what to do when it can't comply, so it doesn't invent.

Debugging

  • Isolate failures: is it comprehension, formatting, or knowledge? Fix the right layer.
  • Test on a small labeled set; change one thing at a time; keep a prompt changelog.
  • Watch for prompt injection when inputs are untrusted — keep instructions and data separated.

Guidelines

  • Prefer the simplest prompt that works; add structure only where it earns its keep.
  • Provider-specific: for Anthropic/Claude specifics, consult the claude-api skill.

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