Improve LLM outputs with proven prompt engineering techniques. This skill provides patterns for chain-of-thought reasoning, few-shot learning, and template systems that make AI interactions more reliable and controllable.
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測試它
正在使用「prompt-engineering-patterns」。 Design a prompt that helps users write professional emails
預期結果:
A structured email writing template with role definition, tone guidelines, and format sections that produces consistent, professional email outputs.
正在使用「prompt-engineering-patterns」。 How can I improve code review quality with AI?
預期結果:
A few-shot prompt providing code review examples with common bug patterns, security considerations, and best practices that guides the AI to provide thorough, constructive feedback.
安全審計
安全All 216 static findings are false positives. The flagged files are markdown documentation (.md) and example JSON files containing educational content about prompt engineering techniques. The scanner incorrectly interprets backticks in markdown code blocks as shell commands, text references to cryptographic terms as weak crypto implementations, and tutorial references to system commands as reconnaissance. This is a documentation skill with no executable security issues.
低風險問題 (4)
品質評分
你能建構什麼
Build Reliable AI Products
Design production-ready prompt systems with consistent output formats and error handling patterns for AI-powered applications.
Improve Code Generation
Apply structured prompting techniques to get better code completion and generation results from Claude or Codex.
Create AI Training Materials
Develop comprehensive prompt libraries and templates for team-wide AI adoption and best practices.
試試這些提示
Solve this problem step by step:
Problem: {problem}
Think through each step carefully:
1. [First step]
2. [Second step]
3. [Third step]
Final answer:Classify the following input into one of these categories: {categories}
Examples:
{examples}
Now classify this:
Input: {input}
Category:You are {role}. Your task is {task}.
Guidelines:
- {guideline1}
- {guideline2}
- {guideline3}
Output format:
{output_format}Initial request: {request}
Current draft: {draft}
Feedback to address:
{feedback}
Please revise the draft based on this feedback:最佳實務
- Start with clear role definitions in system prompts to establish AI behavior boundaries
- Use specific, diverse examples in few-shot prompts that cover edge cases
- Test prompts across multiple query variations to ensure consistent outputs
避免
- Using vague instructions like 'be helpful' without specific behavioral guidelines
- Overloading prompts with too many examples that exceed context window limits
- Assuming prompts work identically across different LLM models without testing