Goal

• Your goal is to reduce the text in the slide text without changing content or structure

Workflow

Role

• Your role is specified in .claude/skills/role.ai_researcher.md

Follow Conventions

• Follow the conventions in .claude/skills/slides.rules.md
  • E.g., if a line starts with an asterisk *, it's the slide title and leave it unchanged

Keep the Structure

• Maintain the sequence of slides, the comments, and the headers
• Maintain the structure of the text in terms of bullet and sub-bullet points
• Keep all the figures
• Leave bold lines untouched
• Never collapse a sub-point into a colon-joined clause on the parent bullet: shorten the words in each bullet, don't merge bullets together
  • Bad

    - **Collections of data**: organized datasets for analysis
      - E.g., customer purchase histories in CRM
    

  • Good

    - **Collections of data**
      - Organized datasets for analysis
      - E.g., customer purchase histories in CRM
    

Reduce Text

• Reduce text keeping the structure of the bullets untouched
  • Write directly to the audience using "you"
  • Be concise: remove filler words (e.g., "the", "that", "very")
  • Use active voice (e.g., "Improve accuracy," not "Accuracy can be improved")
  • Prefer short phrases over full sentences
• Delete a whole bullet point outright when it is unclear or adds no information beyond a nearby bullet, rather than trying to trim it
  • E.g., for a slide in input like:

    * Slide title
    - This is a very long bullet point that is not clear and should be removed
    - This is a clear bullet point that should be kept
    

    the output is:

    * Slide title
    - This is a clear bullet point that should be kept
    

Example

• Input

  - **Collections of data**
    - Aggregated, organized data sets for analysis
    - E.g., customer purchase histories in a CRM system
  
  - **Descriptive statistics**
    - Summary metrics: mean, median, mode, standard deviation
    - E.g., average sales per quarter to understand trends
  
  - **Historical reports**
    - Examination of _past performance_
    - E.g., monthly sales reports for past fiscal year
  
  - **Dashboards**
    - Visual displays of key metrics for insights
    - E.g., dashboard showing quarterly revenue, expenses
  
  - **Models**
    - Statistical representations to _forecast, explain phenomena_
    - E.g., model to anticipate customer churn based on behavioral data
  

• Output

  - **Collections of data**
    - Organized datasets for analysis
    - E.g., customer purchase histories in a CRM
  
  - **Descriptive statistics**
    - Key metrics: mean, median, mode, standard deviation
    - E.g., average quarterly sales to track trends
  
  - **Historical reports**
    - Review of _past performance_
    - E.g., monthly sales reports from last year
  
  - **Dashboards**
    - Visuals of key metrics for quick insights
    - E.g., quarterly revenue and expense dashboard
  
  - **Models**
    - Statistical tools to _forecast, explain_
    - E.g., churn prediction from customer behavior