功能描述

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 patterns. Supports multimodal inputs (images, audio), Python/TypeScript/Ruby/Go, 10+ frameworks, 50-70% token optimization, 95%+ compilation success.

使用说明 (SKILL.md)

BAML Code Generation

Name: baml-codegen
Author: killerapp

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\x3Cllm> GPT5 { provider openai options { model gpt-5 } retry_policy Exponential }`
Fallback	`client\x3Cllm> Resilient { provider fallback options { strategy [FastModel, SlowModel] } }`

Types

Primitives: string, int, float, bool | Multimodal: image, audio
Containers: Type[] (array), Type? (optional), map\x3Cstring, 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

BAML Code - Complete .baml files (types, functions, clients, retry_policy)
Tests - pytest/Jest with 100% function coverage
Integration - Framework-specific client code (LangGraph nodes, FastAPI endpoints, Next.js API routes)
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

安全使用建议

This skill appears to be a detailed BAML code-generation recipe, but there are important red flags: (1) The SKILL.md assumes you can run `baml-cli` and that MCP/LLM provider access exists, yet the skill metadata declares no required binaries or credentials. (2) The source is unknown and there is no homepage; you cannot verify origin or upstream code. Before installing or enabling: - Confirm you trust the publisher or obtain the upstream repository/homepage. - Ensure `baml-cli` and any language toolchains are installed from official sources; do not run arbitrary install links. - Provide LLM provider API keys only to parts of your system you control; do not hand credentials to an unknown remote. - Run this skill in a sandboxed project or disposable environment first (so generated code and any post-generation hooks cannot affect unrelated files). - If you expect the skill to query MCP or provider endpoints automatically, require that it declare those endpoints and explicit env vars in metadata; consider asking the author to add required env var declarations and an install spec. If you want, I can list exactly what env vars and binaries would be reasonable to require for this skill (e.g., BAML_CLI, OPENAI_API_KEY, ANTHROPIC_API_KEY, MCP_ENDPOINT) and suggest a minimal secure run checklist.

功能分析

Type: OpenClaw Skill Name: baml-codegen Version: 2.0.0 The skill is designed to fetch BAML code patterns from external GitHub repositories (BoundaryML/baml-examples, BoundaryML/baml) using MCP tools like `mcp__baml_Examples__fetch_generic_url_content` as detailed in `references/mcp-interface.md`. These fetched patterns are then used by `baml-cli generate` to produce executable client code in various languages (Python, TypeScript, Go, Ruby) on the user's system. This generation process can trigger `on_generate` hooks (e.g., `black . && isort .`, `gofmt -w . && go mod tidy`, `prettier --write .`) which execute arbitrary shell commands locally. While the skill explicitly states it queries 'official BoundaryML repositories', this mechanism introduces a supply chain risk and a potential prompt-injection vulnerability where a malicious prompt could redirect the agent to fetch and generate code from an untrusted source, leading to local code execution.

能力评估

⚠ Purpose & Capability

The skill claims to query MCP servers and to require running `baml-cli generate` (and to integrate with LLM providers). Yet the registry metadata declares no required binaries, no required environment variables, and no install specification. That mismatch is unexpected: a codegen workflow that invokes a CLI and cloud LLM providers would normally declare those dependencies and credentials.

ℹ Instruction Scope

SKILL.md is detailed and constrained to BAML generation tasks (edit baml_src/, generate baml_client/, run tests, integrate with frameworks). It instructs the agent to run `baml-cli generate`, manage project files, and use provider clients. It does not explicitly instruct exfiltration or access unrelated system paths, but it assumes ability to run CLIs, access networked MCP servers, and use LLM provider credentials that are not declared.

ℹ Install Mechanism

There is no install spec (instruction-only), which reduces risk from arbitrary downloads. However, the instructions assume the existence of external tooling (`baml-cli`, language runtimes, package managers) without declaring them in metadata or providing safe install sources.

⚠ Credentials

The skill references many LLM providers (openai, anthropic, gemini, bedrock, ollama, openai-generic) and MCP servers in prose, but requires.env is empty and no primary credential is declared. Requesting zero credentials while instructing use of cloud LLM providers and MCP queries is disproportionate and inconsistent — the skill will need API keys and network access in practice.

✓ Persistence & Privilege

Flags show normal defaults (always:false, agent invocation allowed). The skill does not request permanent presence or system-wide config changes in the metadata or SKILL.md. It instructs editing project files (baml_src/) and generating baml_client/ which is expected for a generator.

版本历史

v2.0.0

From Foundry: Use when generating BAML code for type-safe LLM extraction, classification, RAG,

元数据

Slug baml-codegen

版本 2.0.0

许可证 —

累计安装 0

当前安装数 0

历史版本数 1

常见问题

baml-codegen 是什么？

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 patterns. Supports multimodal inputs (images, audio), Python/TypeScript/Ruby/Go, 10+ frameworks, 50-70% token optimization, 95%+ compilation success. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 1424 次。

如何安装 baml-codegen？

在 OpenClaw 或 Claude Code 对话框中运行命令「/install baml-codegen」即可一键安装，无需额外配置。

baml-codegen 是免费的吗？

是的，baml-codegen 完全免费（开源免费），可自由下载、安装和使用。

baml-codegen 支持哪些平台？

baml-codegen 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。

谁开发了 baml-codegen？

由 Vaskin Kissoyan（@killerapp）开发并维护，当前版本 v2.0.0。

baml-codegen