Skill: 371166758-qq/qf-prompt-optimizer (version 1.0.0)

# 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


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)