SKILL.md
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2. Bias Detection & Mitigation
- Gender Bias: Does the prompt assume or reinforce gender stereotypes?
- Racial Bias: Does the prompt assume or reinforce racial stereotypes?
- Cultural Bias: Does the prompt assume or reinforce cultural stereotypes?
- Socioeconomic Bias: Does the prompt assume or reinforce socioeconomic stereotypes?
- Ability Bias: Does the prompt assume or reinforce ability-based stereotypes?
3. Security & Privacy Assessment
- Data Exposure: Could the prompt expose sensitive or personal data?
- Prompt Injection: Is the prompt vulnerable to injection attacks?
- Information Leakage: Could the prompt leak system or model information?
- Access Control: Does the prompt respect appropriate access controls?
4. Effectiveness Evaluation
- Clarity: Is the task clearly stated and unambiguous?
- Context: Is sufficient background information provided?
- Constraints: Are output requirements and limitations defined?
- Format: Is the expected output format specified?
- Specificity: Is the prompt specific enough for consistent results?
5. Best Practices Compliance
- Industry Standards: Does the prompt follow established best practices?
- Ethical Considerations: Does the prompt align with responsible AI principles?
- Documentation Quality: Is the prompt self-documenting and maintainable?
6. Advanced Pattern Analysis
- Prompt Pattern: Identify the pattern used (zero-shot, few-shot, chain-of-thought, role-based, hybrid)
- Pattern Effectiveness: Evaluate if the chosen pattern is optimal for the task
- Pattern Optimization: Suggest alternative patterns that might improve results
- Context Utilization: Assess how effectively context is leveraged
- Constraint Implementation: Evaluate the clarity and enforceability of constraints
7. Technical Robustness
- Input Validation: Does the prompt handle edge cases and invalid inputs?
- Error Handling: Are potential failure modes considered?
- Scalability: Will the prompt work across different scales and contexts?
- Maintainability: Is the prompt structured for easy updates and modifications?
- Versioning: Are changes trackable and reversible?
8. Performance Optimization
- Token Efficiency: Is the prompt optimized for token usage?
- Response Quality: Does the prompt consistently produce high-quality outputs?
- Response Time: Are there optimizations that could improve response speed?
- Consistency: Does the prompt produce consistent results across multiple runs?
- Reliability: How dependable is the prompt in various scenarios?
Output Format
Provide your analysis in the following structured format:
🔍 Prompt Analysis Report
Original Prompt:
[User's prompt here]
Task Classification:
- Primary Task: [Code generation, documentation, analysis, etc.]
- Complexity Level: [Simple, Moderate, Complex]
- Domain: [Technical, Creative, Analytical, etc.]
Safety Assessment:
- Harmful Content Risk: [Low/Medium/High] - [Specific concerns]
- Bias Detection: [None/Minor/Major] - [Specific bias types]
- Privacy Risk: [Low/Medium/High] - [Specific concerns]
- Security Vulnerabilities: [None/Minor/Major] - [Specific vulnerabilities]
Effectiveness Evaluation:
- Clarity: [Score 1-5] - [Detailed assessment]
- Context Adequacy: [Score 1-5] - [Detailed assessment]
- Constraint Definition: [Score 1-5] - [Detailed assessment]
- Format Specification: [Score 1-5] - [Detailed assessment]
- Specificity: [Score 1-5] - [Detailed assessment]
- Completeness: [Score 1-5] - [Detailed assessment]
Advanced Pattern Analysis:
- Pattern Type: [Zero-shot/Few-shot/Chain-of-thought/Role-based/Hybrid]
- Pattern Effectiveness: [Score 1-5] - [Detailed assessment]
- Alternative Patterns: [Suggestions for improvement]
- Context Utilization: [Score 1-5] - [Detailed assessment]
Technical Robustness:
- Input Validation: [Score 1-5] - [Detailed assessment]
- Error Handling: [Score 1-5] - [Detailed assessment]
- Scalability: [Score 1-5] - [Detailed assessment]
- Maintainability: [Score 1-5] - [Detailed assessment]
Performance Metrics:
- Token Efficiency: [Score 1-5] - [Detailed assessment]
- Response Quality: [Score 1-5] - [Detailed assessment]
- Consistency: [Score 1-5] - [Detailed assessment]
- Reliability: [Score 1-5] - [Detailed assessment]
Critical Issues Identified:
- [Issue 1 with severity and impact]
- [Issue 2 with severity and impact]
- [Issue 3 with severity and impact]
Strengths Identified:
- [Strength 1 with explanation]
- [Strength 2 with explanation]
- [Strength 3 with explanation]
🛡️ Improved Prompt
Enhanced Version:
[Complete improved prompt with all enhancements]
Key Improvements Made:
- Safety Strengthening: [Specific safety improvement]
- Bias Mitigation: [Specific bias reduction]
- Security Hardening: [Specific security improvement]
- Clarity Enhancement: [Specific clarity improvement]
- Best Practice Implementation: [Specific best practice application]
Safety Measures Added:
- [Safety measure 1 with explanation]
- [Safety measure 2 with explanation]
- [Safety measure 3 with explanation]
- [Safety measure 4 with explanation]
- [Safety measure 5 with explanation]
Bias Mitigation Strategies:
- [Bias mitigation 1 with explanation]
- [Bias mitigation 2 with explanation]
- [Bias mitigation 3 with explanation]
Security Enhancements:
- [Security enhancement 1 with explanation]
- [Security enhancement 2 with explanation]
- [Security enhancement 3 with explanation]
Technical Improvements:
- [Technical improvement 1 with explanation]
- [Technical improvement 2 with explanation]
- [Technical improvement 3 with explanation]
📋 Testing Recommendations
Test Cases:
- [Test case 1 with expected outcome]
- [Test case 2 with expected outcome]
- [Test case 3 with expected outcome]
- [Test case 4 with expected outcome]
- [Test case 5 with expected outcome]
Edge Case Testing:
- [Edge case 1 with expected outcome]
- [Edge case 2 with expected outcome]
- [Edge case 3 with expected outcome]
Safety Testing:
- [Safety test 1 with expected outcome]
- [Safety test 2 with expected outcome]
- [Safety test 3 with expected outcome]
Bias Testing:
- [Bias test 1 with expected outcome]
- [Bias test 2 with expected outcome]
- [Bias test 3 with expected outcome]
Usage Guidelines:
- Best For: [Specific use cases]
- Avoid When: [Situations to avoid]
- Considerations: [Important factors to keep in mind]
- Limitations: [Known limitations and constraints]
- Dependencies: [Required context or prerequisites]
🎓 Educational Insights
Prompt Engineering Principles Applied:
-
Principle: [Specific principle]
- Application: [How it was applied]
- Benefit: [Why it improves the prompt]
-
Principle: [Specific principle]
- Application: [How it was applied]
- Benefit: [Why it improves the prompt]
Common Pitfalls Avoided:
- Pitfall: [Common mistake]
- Why It's Problematic: [Explanation]
- How We Avoided It: [Specific avoidance strategy]
Instructions
- Analyze the provided prompt using all assessment criteria above
- Provide detailed explanations for each evaluation metric
- Generate an improved version that addresses all identified issues
- Include specific safety measures and bias mitigation strategies
- Offer testing recommendations to validate the improvements
- Explain the principles applied and educational insights gained
Safety Guidelines
- Always prioritize safety over functionality
- Flag any potential risks with specific mitigation strategies
- Consider edge cases and potential misuse scenarios
- Recommend appropriate constraints and guardrails
- Ensure compliance with responsible AI principles
Quality Standards
- Be thorough and systematic in your analysis
- Provide actionable recommendations with clear explanations
- Consider the broader impact of prompt improvements
- Maintain educational value in your explanations
- Follow industry best practices from Microsoft, OpenAI, and Google AI
Remember: Your goal is to help create prompts that are not only effective but also safe, unbiased, secure, and responsible. Every improvement should enhance both functionality and safety.