How does it improve token efficiency?

The skill applies advanced optimization strategies, including symbol tuning and attribute consolidation, achieving a 50-70% reduction in token usage compared to standard baseline prompts.

What is the BAML Code Generation skill?

This is a specialized capability for Claude Code that automates the creation of BAML applications. It generates complete type definitions, functions, and multi-language client configurations for structured AI data workflows.

When should I use this skill?

Use this skill whenever you need to implement reliable data extraction, text classification, RAG systems, or AI agents that require type-safe, structured outputs from Large Language Models.

Which programming languages and frameworks are supported?

It provides production-ready client code for 6 major languages (Python, TypeScript, Ruby, Java, Go, and C#) and supports integration with over 10 popular development frameworks.

How does it ensure code accuracy?

The skill utilizes a five-layer validation pipeline and real-time MCP integration to query official BoundaryML documentation, ensuring all generated code follows the most current syntax and best practices.

BAML Code Generation

Name: BAML Code Generation
Author: FryrAI

byFryrAI

•

Data Science & ML

Generates production-ready BAML applications for structured data extraction, classification, and AI agent workflows using real-time patterns from official BoundaryML documentation.

About

BAML Code Generation empowers developers to build sophisticated structured output applications by leveraging official BoundaryML patterns and syntax. By integrating directly with documentation and example repositories via MCP servers, it synthesizes complete, type-safe solutions including definitions, functions, and framework-specific clients. Whether you're building complex RAG systems, multimodal extraction pipelines, or autonomous agent planners, this skill ensures high compilation success and significant token optimization while supporting six major programming languages including Python and TypeScript.

Key Features

Multi-stage pattern recognition for extraction, classification, RAG, and agent workflows
3 GitHub stars
Real-time syntax validation and modernization via official BoundaryML MCP integration
Automated synthesis of type definitions, functions, and multi-language client configurations
Advanced token optimization strategies achieving 50-70% reduction over baseline prompts
Comprehensive five-layer validation pipeline for syntax, type safety, and semantics

Use Cases

Extracting structured entities and data from natural language, images, or audio inputs
Building citation-aware RAG systems with precise source tracking and structured outputs
Developing autonomous agent workflows with complex planning and state management

About

Key Features

Multi-stage pattern recognition for extraction, classification, RAG, and agent workflows
3 GitHub stars
Real-time syntax validation and modernization via official BoundaryML MCP integration
Automated synthesis of type definitions, functions, and multi-language client configurations
Advanced token optimization strategies achieving 50-70% reduction over baseline prompts
Comprehensive five-layer validation pipeline for syntax, type safety, and semantics

Use Cases

Extracting structured entities and data from natural language, images, or audio inputs
Building citation-aware RAG systems with precise source tracking and structured outputs
Developing autonomous agent workflows with complex planning and state management