Baml Scaffolder

BAML Scaffolder

When to Use This Skill

Automatically invoke this skill when:

• User describes wanting to extract structured data from LLM
• User mentions building AI features or LLM integrations
• User asks to create functionality that requires LLM calls
• User describes input/output schema for AI processing
• User wants to classify, summarize, extract, or generate content with AI

Examples That Trigger This Skill

• "I need to extract contact information from emails"
• "Can you help me build a receipt parser?"
• "I want to classify customer feedback into categories"
• "How do I summarize articles using an LLM?"
• "I need to extract structured data from invoices"
• "Create an AI function that analyzes sentiment"

How to Use

1. Understand requirements:

   • What data goes in (text, image, etc.)?
   • What structured output is needed?
   • Which LLM provider to use?
   • Any specific validation or constraints?

2. Check for BAML project:

   • Look for baml_src/ directory
   • If not found, suggest initializing with /baml-toolkit:init

3. Design BAML components:

   • Output type (class or enum)
   • Input parameters
   • Function signature
   • Prompt template

4. Generate BAML code:

   • Create type definitions
   • Write function with proper Jinja prompt
   • Add validation assertions
   • Include test case

5. Add to BAML files:

   • Append to appropriate .baml file
   • Show created code to user
   • Suggest running /baml-toolkit:generate

BAML Patterns

Extraction Pattern

When user wants to extract structured data:

// Define output type
class ContactInfo {
  name string
  email string?
  phone string?
  company string?
}

// Create extraction function
function ExtractContact(text: string) -> ContactInfo {
  client GPT4
  prompt #"
    Extract contact information from the following text.
    Return name, email, phone, and company if mentioned.

    Text: {{ text }}
  "#
}

// Add test
test ExtractContact {
  functions [ExtractContact]
  args {
    text "John Smith - john@example.com - Acme Corp"
  }
  assert {
    {
      checks [
        this.name == "John Smith",
        this.email == "john@example.com"
      ]
    }
  }
}

Classification Pattern

When user wants to categorize content:

// Define categories
enum SentimentCategory {
  POSITIVE
  NEGATIVE
  NEUTRAL
  MIXED
}

// Create classifier
function ClassifySentiment(text: string) -> SentimentCategory {
  client GPT4
  prompt #"
    Classify the sentiment of this text.
    Choose: POSITIVE, NEGATIVE, NEUTRAL, or MIXED

    Text: {{ text }}
  "#
}

Generation Pattern

When user wants to generate content:

class GeneratedEmail {
  subject string
  body string
  tone string
}

function GenerateEmail(
  recipient: string,
  purpose: string,
  key_points: string[]
) -> GeneratedEmail {
  client GPT4
  prompt #"
    Generate a professional email.

    Recipient: {{ recipient }}
    Purpose: {{ purpose }}
    Key points to include:
    {% for point in key_points %}
    - {{ point }}
    {% endfor %}

    Return a structured email with subject and body.
  "#
}

Image Analysis Pattern

When user wants to analyze images:

class ImageAnalysis {
  description string
  objects string[]
  text_detected string?
  scene_type string
}

function AnalyzeImage(image: image) -> ImageAnalysis {
  client GPT4Vision
  prompt #"
    Analyze this image in detail.

    Provide:
    - Overall description
    - List of objects/items visible
    - Any text detected (OCR)
    - Type of scene (indoor/outdoor/document/etc)

    {{ _.role('user') }}
    Image: {{ image }}
  "#
}

Decision Making

Choose output type:

• Enum if output is a fixed set of categories
• String/Int/Float if output is a single primitive value
• Class if output is structured with multiple fields
• Array if output is a collection of items

Choose LLM client:

• GPT4 for complex reasoning, high accuracy
• GPT4Vision for image/video inputs
• Claude for long context, coding tasks
• Gemini for multimodal, very long context
• Ollama for local models, privacy

Prompt engineering:

• Be specific about format and requirements
• Include examples in prompt if needed
• Use Jinja variables for dynamic content
• Add role context: {{ _.role('user') }}

Workflow

1. Listen for user describing LLM use case
2. Extract requirements (inputs, outputs, constraints)
3. Design appropriate BAML components
4. Generate well-structured BAML code
5. Add to project files
6. Include test cases
7. Suggest next steps (generate, test, integrate)

Notes

• Always include test cases for validation
• Use descriptive names for types and functions
• Add @description annotations for clarity
• Keep prompts clear and specific
• Suggest appropriate LLM client based on task
• Offer to create multiple variations if requirements are ambiguous

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