matches:
  - trigger: ":prompt-improve"
    replace: |
      You're an expert at prompt engineering.  Please rewrite and improve this prompt to get the best results.

      ## PROMPT WRITING KNOWLEDGE
      
      Tactics:

      Include details in your query to get more relevant answers
      Ask the model to adopt a persona
      Use delimiters to clearly indicate distinct parts of the input
      Specify the steps required to complete a task
      Provide examples
      Specify the desired length of the output
      Provide reference text
      Language models can confidently invent fake answers, especially when asked about esoteric topics or for citations and URLs. In the same way that a sheet of notes can help a student do better on a test, providing reference text to these models can help in answering with fewer fabrications.
      
      Tactics:
      
      Instruct the model to answer using a reference text
      Instruct the model to answer with citations from a reference text
      Split complex tasks into simpler subtasks
      Just as it is good practice in software engineering to decompose a complex system into a set of modular components, the same is true of tasks submitted to a language model. Complex tasks tend to have higher error rates than simpler tasks. Furthermore, complex tasks can often be re-defined as a workflow of simpler tasks in which the outputs of earlier tasks are used to construct the inputs to later tasks.

      - Interpret what the input was trying to accomplish.
      - Read and understand the PROMPT WRITING KNOWLEDGE above.
      - Write and output a better version of the prompt using your knowledge of the techniques above.

      # OUTPUT INSTRUCTIONS:

      1. Output the prompt in clean, human-readable Markdown format.
      2. Only output the prompt, and nothing else, since that prompt might be sent directly into an LLM.

      # INPUT

      The following is the prompt you will improve:
  
  - trigger: ":prompt-rewrite"
    replace: |
      You're an expert technical writer. Rewrite the following text to improve clarity and conciseness while keeping it accurate.  

      **Guidelines:**  
      - Assume your audience has intermediate technical knowledge  
      - Replace jargon with plain language where possible  
      - Break up long sentences  
      - Add bullet points if it helps comprehension  

      Provide **two variations**, and include a 1-sentence explanation of why each is better.

      **Input:**  
      [Insert your text here]  

  - trigger: ":prompt-summarize"
    replace: |
      Summarize this technical content for a stakeholder who isn't an engineer.  

      **Goals:**  
      - Keep it under 100 words  
      - Focus on the "why it matters"  
      - No acronyms unless explained  

      **Example Summary:**  
      “We discovered a performance bottleneck in the database queries, which slowed down our app. We’re optimizing them now to improve user experience.”

      **Input:**  
      [Insert content here]

  - trigger: ":prompt-bugfix"
    replace: |
      Act as a senior Python developer. Help debug this code.

      **Instructions:**  
      1. Identify any bugs or bad practices  
      2. Suggest fixes with brief explanation  
      3. Provide a corrected version  
      4. Suggest improvements for readability  

      **Input Code:**  
      [Paste your Python code here]

  - trigger: ":prompt-qa"
    replace: |
      Based on the following text, generate 5 thoughtful questions that challenge assumptions, test understanding, or uncover edge cases.  
      
      **Context:** Preparing for code reviews and collaborative refinement.  
      
      **Input:**  
      [Insert concept or document]

  - trigger: ":prompt-variations"
    replace: |
      You are a creative writer with a technical background.  

      Generate **3 variations** of this copy, optimized for different tones:  
      - Formal  
      - Friendly  
      - Technical  

      **Input:**  
      [Paste text here]