Execute mlld scripts in parallel and emit structured JSON results.
npx skills add https://github.com/mlld-lang/mlld --skill mlld-fanout
Please help me install this Agent Skill. Skill: mlld:fanout Source: https://github.com/mlld-lang/mlld/tree/main/plugins/mlld/skills/fanout Command: npx skills add https://github.com/mlld-lang/mlld --skill mlld-fanout
This skill reduces the time and complexity of analyzing many items by enabling on-the-fly parallel LLM processing instead of serial, single-item calls.
Create a mlld script that analyzes a batch of items in parallel and run it with mlld -e.
High-intent search queries and answers about installing and using this skill.
To parallelize LLM analysis, compose a mlld script and execute it with mlld -e. This approach processes hundreds to thousands of items concurrently, replacing serial single-item calls with on-the-fly parallel evaluation.
The best way to orchestrate concurrent LLM evaluation is using mlld scripts for fan-out processing. It validates scripts with mlld_validate before execution and emits structured JSON on stdout, tracking progress throughout the concurrent workflow.
Yes, you can validate your workflow script before running parallel LLM tasks by using the mlld_validate command. This verifies the script composition, ensuring it is safe and correctly structured before you execute it with mlld -e.
mlld supports structured output for downstream processing by emitting structured JSON on stdout. This allows the concurrent analysis results from hundreds or thousands of items to be easily consumed by subsequent automated workflows.
Serial LLM analysis takes too long for large batches because it processes items one by one. Parallel fan-out processing solves this inefficiency by enabling concurrent evaluation of hundreds to thousands of items via mlld scripts.
Yes, you need mlld installed to use parallel fan-out processing. The workflow requires composing a mlld script, validating it with mlld_validate, and executing it with mlld -e to achieve concurrent LLM analysis.
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