SkillHub

baml-codegen

v2.0.0

Use when generating BAML code for type-safe LLM extraction, classification, RAG, or agent workflows - creates complete .baml files with types, functions, clients, tests, and framework integrations from natural language requirements. Queries official BoundaryML repositories via MCP for real-time patt...

Sourced from ClawHub, Authored by killerapp

Installation

Please help me install the skill `baml-codegen` from SkillHub official store. npx skills add killerapp/baml-codegen

BAML Code Generation

Generate type-safe LLM extraction code. Use when creating structured outputs, classification, RAG, or agent workflows.

Golden Rules

  • NEVER edit baml_client/ - 100% generated, overwritten on every baml-cli generate; check baml_src/generators.baml for output_type (python, typescript, ruby, go)
  • ALWAYS edit baml_src/ - Source of truth for all BAML code
  • Run baml-cli generate after changes - Regenerates typed client code for target language

Philosophy (TL;DR)

  • Schema Is The Prompt - Define data models first, compiler injects types
  • Types Over Strings - Use enums/classes/unions, not string parsing
  • Fuzzy Parsing Is BAML's Job - BAML extracts valid JSON from messy LLM output
  • Transpiler Not Library - Write .baml → generate native code (Python/TypeScript/Ruby/Go), no runtime dependency
  • Test-Driven Prompting - Use VS Code playground or baml-cli test to iterate

Workflow

Analyze → Pattern Match (MCP) → Validate → Generate → Test → Deliver
         ↓ [IF ERRORS] Error Recovery (MCP) → Retry

BAML Syntax

Element Example
Class class Invoice { total float @description("Amount") @assert(this > 0) @alias("amt") }
Enum enum Category { Tech @alias("technology") @description("Tech sector"), Finance, Other }
Function function Extract(text: string, img: image?) -> Invoice { client GPT5 prompt #"{{ text }} {{ img }} {{ ctx.output_format }}"# }
Client client<llm> GPT5 { provider openai options { model gpt-5 } retry_policy Exponential }
Fallback client<llm> Resilient { provider fallback options { strategy [FastModel, SlowModel] } }

Types

  • Primitives: string, int, float, bool | Multimodal: image, audio
  • Containers: Type[] (array), Type? (optional), map<string, Type> (key-value)
  • Composite: Type1 | Type2 (union), nested classes
  • Annotations: @description("..."), @assert(condition), @alias("json_name"), @check(name, condition)

Providers

openai, anthropic, gemini, vertex, bedrock, ollama + any OpenAI-compatible via openai-generic

Pattern Categories

Pattern Use Case Model Framework Markers
Extraction Unstructured → structured GPT-5 fastapi, next.js
Classification Categorization GPT-5-mini any
RAG Answers with citations GPT-5 langgraph
Agents Multi-step reasoning GPT-5 langgraph
Vision Image/audio data extraction GPT-5-Vision multimodal

Resilience

  • retry_policy: retry_policy Exp { max_retries 3 strategy { type exponential_backoff } }
  • fallback client: Chain models [FastCheap, SlowReliable] for cost/reliability tradeoff

MCP Indicators

  • Found patterns from baml-examples | Validated against BoundaryML/baml | Fixed errors using docs | MCP unavailable, using fallback

Output Artifacts

  1. BAML Code - Complete .baml files (types, functions, clients, retry_policy)
  2. Tests - pytest/Jest with 100% function coverage
  3. Integration - Framework-specific client code (LangGraph nodes, FastAPI endpoints, Next.js API routes)
  4. Metadata - Pattern used, token count, cost estimate

References

  • providers.md - OpenAI, Anthropic, Google, Ollama, Azure, Bedrock, openai-generic
  • types-and-schemas.md - Full type system, classes, enums, unions, map, image, audio
  • validation.md - @assert, @check, @alias, block-level @@assert
  • patterns.md - Pattern library with code examples
  • philosophy.md - BAML principles, golden rules
  • mcp-interface.md - Query workflow, caching
  • languages-python.md - Python/Pydantic, async
  • languages-typescript.md - TypeScript, React/Next.js
  • frameworks-langgraph.md - LangGraph integration