Skill: openlark/ai-prompt-optimization (version 1.0.0)

--- name: ai-prompt-optimization description: Use when users need to optimize prompts for AI conversations, generate structured templates, create few-shot examples, design chain-of-thought guidance, or diagnose and improve existing prompts. Applicable to prompt optimization for various AI tools such as ChatGPT, Claude, Midjourney, etc. --- # AI Prompt Optimization ## Core Capabilities When users seek prompt optimization assistance, provide the following services: 1. **Diagnosis & Optimization** - Analyze existing prompt issues and provide specific improvement plans 2. **Template Generation** - Generate structured prompt templates for different scenarios 3. **Few-Shot Generation** - Create example-driven few-shot prompts 4. **Chain-of-Thought Guidance** - Design CoT (Chain of Thought) prompts ## Usage ### 1. Diagnosis & Optimization Workflow When a user provides a prompt for optimization: ``` Analyze Structure → Identify Issues → Provide Improved Version → Explain Changes ``` **Diagnosis Checklist**: - [ ] Is the role/identity clearly defined? - [ ] Is the task objective specific and clear? - [ ] Are output format/style constrained? - [ ] Is the necessary context/background information provided? - [ ] Are boundary conditions and exceptions specified? - [ ] Are there clear success criteria? ### 2. Template Generation Generate structured templates based on user scenarios. Core template format: ``` # Role Definition You are a [role] in [professional domain], skilled at [core competency]. # Task Description Please help me [specific task], with the goal of [expected outcome]. # Context Information - Background: [relevant background] - Audience: [target users] - Constraints: [boundary conditions] # Output Requirements - Format: [desired format] - Style: [language style] - Length: [length requirement] # Quality Standards [Key metrics for evaluating output] ``` ### 3. Few-Shot Example Generation Generate few-shot examples for complex tasks: 1. **Select Representative Samples** - 3-5 examples covering different variants 2. **Format Examples** - Input → Output structure 3. **Add Explanations** - Explain the rationale for selecting each example ### 4. Chain-of-Thought Design Design CoT prompts for tasks requiring reasoning: ``` Before giving your final answer, please think through the following steps: 1. [Understand the Problem] - ... 2. [Decompose the Problem] - ... 3. [Step-by-Step Reasoning] - ... 4. [Verify the Conclusion] - ... ``` ## Scenario Reference For complete scenario templates and examples, see `references/templates.md`: - Writing assistance prompts - Code generation prompts - Image generation prompts - Data analysis prompts - Q&A and consultation prompts ## Optimization Principles 1. **Specific > Vague** - Clearly specify what is wanted and what is not 2. **Structured > Scattered** - Use clear segmentation and markers 3. **Constrained > Free** - Appropriate constraints improve output quality 4. **Iterative > One-Shot** - Encourage users to continuously optimize based on output


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