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// Skill profile

Prompt Optimizer

Transform vague, underperforming prompts into precise, structured prompts that consistently produce high-quality AI outputs.

by 371166758-qq · published 2026-04-01

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Last updated
2026-04
// Install command
$ claw add gh:371166758-qq/371166758-qq-qf-prompt-optimizer
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// Full documentation

# Prompt Optimizer

Transform vague, underperforming prompts into precise, structured prompts that consistently produce high-quality AI outputs.

Description

This skill takes any user prompt — whether vague, ambiguous, or poorly structured — and systematically refines it into a professional-grade prompt following established prompt engineering principles. It applies techniques from chain-of-thought, role-prompting, few-shot learning, and structured output formatting to maximize AI performance.

When to Use

  • The user provides a vague prompt like "write something about marketing" and expects better results
  • A prompt produces inconsistent or off-topic outputs
  • Converting natural language requests into structured prompts
  • Building prompt templates for repeated use
  • Debugging prompts that fail in edge cases
  • Instructions

    The OPTIMIZE Framework

    When refining a prompt, apply these six principles in order:

    #### O — Objective (明确目标)

    **Problem**: Vague verbs like "write about," "explain," "help with"

    **Fix**: Specify exact deliverable and success criteria

    | Vague | Optimized |

    |-------|-----------|

    | "Write about AI" | "Write a 500-word blog post explaining how large language models work, targeting software developers with 2+ years of experience" |

    | "Make it better" | "Improve clarity and reduce sentence length by 30% while preserving all technical details" |

    | "Fix the code" | "Refactor this Python function to reduce cyclomatic complexity below 5 and add type hints" |

    #### P — Persona (设定角色)

    Assign a specific role to ground the AI's expertise:

  • "You are a senior staff engineer at Google with 15 years of distributed systems experience"
  • "You are a Nature journal reviewer specializing in immunology"
  • "You are a direct-response copywriter trained by Eugene Schwartz's methods"
  • Include constraints: "Respond only with what you're confident about. If uncertain, say so."

    #### T — Task Structure (任务结构)

    Break complex tasks into ordered steps:

    1. First, analyze X and identify Y
    2. Then, based on Y, generate Z using method A
    3. Finally, format the output as...

    For multi-step tasks, use numbered steps rather than one compound instruction.

    #### I — Input Specification (输入规范)

    Define what the user will provide:

  • "I will provide: (1) a product description, (2) target audience, (3) competitor list"
  • "Input: A CSV file with columns [date, revenue, expenses]"
  • "Here is the code to review: ```<language>\n...\n```"
  • Explicit input templates reduce ambiguity.

    #### M — Metrics & Constraints (约束条件)

    Add specific constraints:

    Constraints:
    - Maximum 500 words
    - Use only peer-reviewed sources
    - No jargon; explain all technical terms
    - Output in Chinese
    - Format as a comparison table
    - Must include 3 concrete examples

    #### I — Ideal Output (理想输出)

    Show or describe the desired output format:

  • Provide an example of expected output (few-shot)
  • Specify format: JSON schema, markdown table, numbered list, code block
  • Define evaluation criteria: "The output is successful if a non-expert can understand the explanation"
  • Prompt Optimization Process

    Given a raw prompt, produce:

    1. **Diagnosis**: What's wrong with the original (vague goal? missing context? no format? no constraints?)

    2. **Optimized Prompt**: The refined version following OPTIMIZE framework

    3. **Explanation**: What was changed and why

    Common Anti-Patterns

    | Anti-Pattern | Problem | Fix |

    |-------------|---------|-----|

    | "Just make it good" | No quality criteria | Define what "good" means with measurable criteria |

    | Giant wall of text | AI loses focus | Break into numbered sections with clear headers |

    | Contradictory instructions | AI guesses priority | Remove conflicts; rank priorities explicitly |

    | Missing negative constraints | AI makes unwanted assumptions | Add "Do NOT..." instructions for known failure modes |

    | No examples | AI style is unpredictable | Provide 1-3 input/output examples |

    Examples

    **Raw Prompt**: "Write an email to my boss asking for a raise"

    **Optimized Prompt**:

    You are a professional career coach helping a software engineer draft a salary negotiation email.
    
    Task: Write a salary increase request email to my manager.
    
    Context:
    - I'm a mid-level software engineer, 2 years at the company
    - I recently led a project that saved the company $200K annually
    - The company just closed a successful funding round
    - My current salary is below market rate based on Levels.fyi data
    
    Requirements:
    - Professional but warm tone (not aggressive, not passive)
    - 150-250 words
    - Lead with value delivered, not personal needs
    - Include a specific meeting request
    - No ultimatums or comparisons with colleagues
    
    Format: Standard email with subject line

    **Raw Prompt**: "分析这个数据"

    **Optimized Prompt**:

    You are a senior data analyst. Analyze the provided dataset and produce a business report.
    
    Input: I will provide a CSV file with monthly sales data (columns: date, product, quantity, revenue, region).
    
    Steps:
    1. Identify the top 3 revenue-generating products
    2. Detect any seasonal trends or anomalies
    3. Compare regional performance
    4. Provide 3 actionable business recommendations
    
    Output format:
    - Executive summary (3 sentences)
    - Key findings as a numbered list
    - Recommendations with expected impact (high/medium/low)
    - Any data quality concerns
    
    Language: Chinese

    Tips

  • The best prompts read like briefs given to a competent professional, not commands given to a machine
  • Always test optimized prompts with edge cases before standardizing
  • Keep prompts under 500 words when possible — longer prompts can confuse the model
  • Version your prompts (v1, v2) and track which versions produce better results
  • When a prompt still fails after optimization, the task may need to be decomposed into subtasks
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