A specialized skill for generating type-safe BAML code to handle structured LLM outputs, classification, and RAG workflows with 95% compilation success.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install baml-codegen
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install baml-codegen using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is BAML Code Generation?
BAML Code Generation is a professional-grade tool designed for developers who need to bridge the gap between natural language requirements and type-safe LLM integrations within their Openclaw Skills projects. It functions as a transpiler rather than a traditional library, allowing users to define data models and prompts in .baml files which are then compiled into native Python, TypeScript, Ruby, or Go code. This ensures that the schema remains the source of truth, providing robust fuzzy parsing capabilities to extract valid JSON from even the most unstructured LLM outputs.
By incorporating this skill into your workflow, you gain access to optimized token usage and high-performance LLM interactions. It simplifies the lifecycle of prompt engineering and structured data extraction, making it an essential component for any developer utilizing Openclaw Skills for production-ready AI applications that require strict data integrity and multimodal capabilities.
BAML Code Generation Use Cases
- Converting unstructured text into structured JSON objects using type-safe classes.
- Implementing resilient classification systems with built-in retry policies and fallback clients.
- Building advanced RAG pipelines that require precise citations and structured answers.
- Developing multimodal agent workflows that process both text and visual/audio inputs.
- Standardizing LLM prompts across multiple programming languages like Python and TypeScript.
How BAML Code Generation Works
- Requirement Analysis: The skill analyzes natural language requirements to determine the necessary data models and logic.
- Pattern Matching: It queries official BoundaryML repositories via MCP to find the most efficient code patterns.
- Schema Definition: The skill generates BAML source files in the baml_src directory, defining classes, enums, and functions.
- Validation & Generation: It validates the code against BoundaryML standards and runs the BAML compiler to generate native client code.
- Automated Testing: The workflow concludes by generating and running unit tests to ensure high compilation success.
BAML Code Generation Setup
To get started with this skill in your Openclaw Skills environment, ensure you have the necessary MCP servers configured and follow these steps:
# Install the BAML CLI
npm install -g @boundaryml/baml-cli
# Initialize BAML in your project directory
baml-cli init
# Generate the typed client after defining your .baml files
baml-cli generate
BAML Code Generation Data Schema & Taxonomy
The skill organizes data within a strict directory structure to maintain a clear source of truth:
| Directory/File | Purpose |
|---|---|
baml_src/ |
Primary source directory containing .baml files for types, functions, and clients. |
baml_client/ |
Generated native code (Python/TS/Ruby/Go). This is overwritten on every generation. |
baml_src/generators.baml |
Configuration for output language and generation settings. |
tests/ |
Auto-generated test cases for verifying LLM extraction logic and function coverage. |
BAML Code Generation Advanced Features
- Multimodal processing support for extracting data directly from images and audio files.
- Intelligent fallback mechanisms to chain different LLM providers for cost and reliability trade-offs.
- Advanced validation using @assert and @check annotations to enforce data constraints at the schema level.
- Integration with LangGraph for complex multi-step reasoning and agentic workflows.
- High-performance token optimization reducing context overhead by 50-70% in Openclaw Skills deployments.
SKILL.md
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