What document types does this skill support?

It features specialized patterns for PDFs, Markdown, source code, and large complex document sets, ensuring optimal parsing and embedding strategies for each format.

How does it handle model and embedding providers?

The skill utilizes standardized templates for providers like Ollama, OpenAI, and Universal models, automatically configuring the correct dimensions and retrieval strategies.

Is the generated YAML code validated?

Yes, the generation workflow includes an automatic validation step using the /validate command to ensure the configuration is syntactically correct and functional.

How do I trigger the LlamaFarm configuration generation?

You can activate the skill by running the /config command or by describing your project goals, such as 'Create a new project for PDF analysis' or 'Add a markdown parser to my config'.

Can I use this to update an existing configuration?

Yes. The skill detects modification intents like 'add', 'change', 'update', or 'remove' to help you iteratively refine your LlamaFarm setup.

LlamaFarm Configuration Builder

Name: LlamaFarm Configuration Builder
Author: llama-farm

byllama-farm

데이터 과학 및 ML

Generates optimized LlamaFarm configuration files from natural language descriptions for RAG and document processing workflows.

소개

The LlamaFarm Configuration Builder skill streamlines the setup of LlamaFarm projects by converting high-level user intent into precise, production-ready YAML configurations. It intelligently detects specific use cases—such as regulatory PDF analysis, source code indexing, or large-scale document management—and applies domain-specific patterns for models, embedding strategies, and data processing. By automating complex boilerplate and providing built-in validation, this skill enables developers to rapidly deploy sophisticated AI-driven document intelligence systems without the friction of manual configuration.

주요 기능

Natural language to YAML configuration generation
Template-based model and embedding strategy selection
Automatic use case detection for PDF, Markdown, and Code patterns
Built-in configuration validation via the /validate workflow
Support for both new project bootstrapping and existing config modification
0 GitHub stars

사용 사례

Setting up a mixed-format data processing engine with specialized embedding models
Configuring a code analysis pipeline to index local source code repositories
Bootstrapping a RAG project for a library of regulatory PDF or legal documents

소개

주요 기능

Natural language to YAML configuration generation
Template-based model and embedding strategy selection
Automatic use case detection for PDF, Markdown, and Code patterns
Built-in configuration validation via the /validate workflow
Support for both new project bootstrapping and existing config modification
0 GitHub stars

사용 사례

Setting up a mixed-format data processing engine with specialized embedding models
Configuring a code analysis pipeline to index local source code repositories
Bootstrapping a RAG project for a library of regulatory PDF or legal documents