mlld-lang
Official@mlld-lang
Orchestrate prompt-driven decision logic and parallel execution patterns for complex data retrieval and modular task processing.
Agent Skills by mlld-lang
Showing 5 vetted skills indexed across 1 GitHub repositories.
mlld-langmlld:llm-first
Replace hard-coded branching with prompt-driven decision orchestration in mlld pipelines.
mlld-langmlld:fanout
Execute mlld scripts in parallel and emit structured JSON results.
mlld-langmlld:agents
Design and deploy modular LLM agents with @meta and @taskPrompt exports.
mlld-langmlld:query
Generate and execute SQL or code queries across databases, CSVs, and files.
mlld-langmlld:orchestrator
Design and build mlld orchestrators for LLM workflows with parallel calls and checkpoints.
Frequently Asked Questions About mlld-lang
FAQPage SchemaWhat specific tasks can be performed using mlld-lang?▼
Users can replace hard-coded branching with prompt-driven decision orchestration, execute parallel tasks across databases or files, and generate structured JSON results. It enables the design of modular components with specific metadata exports for complex, multi-step processing requirements.
Who is the target persona for these capabilities?▼
The target persona includes data engineers and system architects focused on replacing rigid conditional logic with dynamic, prompt-based decisioning. It is designed for developers building distributed processing systems that require parallel execution and standardized data output across heterogeneous environments.
What are the core prerequisites for implementing these capabilities?▼
Implementation requires an existing environment capable of executing the mlld framework. Users must define task prompts and metadata exports using the provided @meta and @taskPrompt syntax to ensure compatibility with the orchestrator and parallel execution modules.