Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Developer Tools
baml-codegen
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.
skill openclawclawhub Free
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.
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, assets/agents/README.md, assets/classification/README.md, assets/extraction/README.md, assets/integrations/README.md, assets/rag/README.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 2.0.0
Provenance
- Publisher
- killerapp
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
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 โ 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 โ Pattern Match (MCP) โ Validate โ Generate โ Test โ Deliver โ [IF ERRORS] Error Recovery (MCP) โ Retry
BAML Syntax
ElementExampleClassclass Invoice { total float @description("Amount") @assert(this > 0) @alias("amt") }Enumenum Category { Tech @alias("technology") @description("Tech sector"), Finance, Other }Functionfunction Extract(text: string, img: image?) -> Invoice { client GPT5 prompt #"{{ text }} {{ img }} {{ ctx.output_format }}"# }Clientclient<llm> GPT5 { provider openai options { model gpt-5 } retry_policy Exponential }Fallbackclient<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
PatternUse CaseModelFramework MarkersExtractionUnstructured โ structuredGPT-5fastapi, next.jsClassificationCategorizationGPT-5-minianyRAGAnswers with citationsGPT-5langgraphAgentsMulti-step reasoningGPT-5langgraphVisionImage/audio data extractionGPT-5-Visionmultimodal
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
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
6 Docs
- SKILL.md
- assets/agents/README.md
- assets/classification/README.md
- assets/extraction/README.md
- assets/integrations/README.md
- assets/rag/README.md