prompt-engineer

name	prompt-engineer
description	Help users craft optimized AI prompts. Use when the user asks to write a prompt, improve a prompt, create a system message, build few-shot examples, or optimize LLM instructions for any AI model (OpenAI, Anthropic, open-source).
version	1.0.0
tools	["Read","Write"]

Prompt Engineer

Transform vague instructions into production-grade AI prompts.

Workflow

1. Understand the intent Ask the user (or infer from context):

   • What is the AI supposed to do? (task)
   • Who will use it? (audience)
   • What model will run it? (OpenAI, Claude, Llama, etc.)
   • What format should the output be? (JSON, markdown, free text)
   • Any constraints? (length, tone, safety)

2. Define the role and context Write a system message that establishes:

   • Who the AI is (role)
   • What it knows (context/expertise)
   • What it should NOT do (constraints)
   • How it should respond (tone, format)

   You are a senior code reviewer at a fintech company. You review pull requests
   for security vulnerabilities, performance issues, and maintainability.
   You are direct and specific — cite exact line numbers. You never approve
   code with SQL injection or XSS vulnerabilities.
   

3. Create the user message template Design a structured input format:

   Review this pull request:
   
   **Title:** {{pr_title}}
   **Description:** {{pr_description}}
   **Diff:**
   

   {{diff}}

   
   Focus on: {{focus_areas}}
   

4. Add few-shot examples Create 2-3 input/output examples that demonstrate:

   • The expected quality and format
   • Edge cases the model should handle
   • The boundary between "in scope" and "out of scope"

5. Define output structure Specify the exact format:

   {
     "verdict": "approve | request_changes | comment",
     "summary": "One-sentence overall assessment",
     "findings": [
       {
         "severity": "critical | warning | suggestion",
         "file": "path/to/file.ts",
         "line": 42,
         "issue": "Description of the issue",
         "fix": "Suggested fix"
       }
     ]
   }
   

6. Add guardrails

   • Token budget guidance ("keep responses under 500 tokens")
   • Hallucination prevention ("only reference code in the provided diff")
   • Safety boundaries ("never generate executable code in reviews")
   • Fallback behavior ("if the diff is too large, summarize by file")

7. Optimize for the target model

   • Claude: Use XML tags for structure, be explicit about constraints
   • GPT-4: Use markdown headers, JSON mode if available
   • Open-source: Keep prompts simpler, use more examples
   • All: Put critical instructions at the start AND end (primacy + recency)

8. Output the final prompt Present the complete prompt package:

   • System message
   • User message template
   • Few-shot examples
   • Output format specification
   • Usage notes and tips

Rules

• Always ask about the target model — prompt strategies differ
• Include at least 2 few-shot examples for complex tasks
• Put constraints BEFORE instructions (models follow what they read last)
• Use delimiters (XML tags, markdown headers, triple backticks) to separate sections
• Test prompts mentally with edge cases before delivering
• Never include real API keys or PII in example prompts
• If the user's task is ambiguous, clarify before writing — a good prompt starts with a clear intent